If you’re running modern applications on AWS, you already have access to more data than ever: metrics, logs, traces, and events. The real advantage comes from turning that data into actionable insights that drive continuous improvement.

Today’s challenge occurs when something breaks; teams still spend too much time connecting the dots. Pulling data from different tools, correlating signals, and trying to figure out what changed. That gap between insight and action is where time is lost, and customer impact grows.

Dynatrace and AWS are working together to close that gap.

With the introduction of AWS DevOps Agent and deeper integrations across Dynatrace and AWS services like Kiro, the experience moves beyond monitoring into AI-powered observability. It becomes a connected system that detects issues, investigates them, and feeds directly into how teams fix and improve software.

How we got here

At AWS re:Invent, AWS introduced DevOps Agent, a new type of AI agent built to investigate and resolve issues across cloud environments. Instead of relying on manual troubleshooting, the agent works continuously across services to understand what changed and why. Dynatrace has been part of that effort since the start. After all, if these agents are going to be effective, they need real production context. That’s the Dynatrace specialty.

That collaboration evolved quickly. Sharing observability data is now a two-way integration. Dynatrace detects and understands issues. AWS DevOps Agent investigates them. The results flow directly back into Dynatrace for a complete view of what happened and what to do next.

The next iteration of the Clouds SRE app

As customers started using the initial implementation, one thing became clear. The foundation was there, but there was an opportunity to make the experience more streamlined, more transparent, and more aligned with how teams actually operate at scale.

With the introduction of the Clouds SRE app, that experience has evolved in a meaningful way.

Instead of requiring users to manually configure multiple workflows, onboarding is now guided. Teams can get started faster without needing to define everything upfront. What used to take several workflows is now simplified into a more focused, purpose-built experience across AWS.

Visibility is another big step forward. Previously, there was limited insight into what agents were doing during an investigation. Now, teams have transparent tracking into agent activity, including approvals, notifications, and the ability to automatically re-run stalled investigations. That shift alone gives teams more confidence in how work is executed.

Routing and management have also become much more intuitive. Rather than managing workflows behind the scenes, teams can now use interaction profiles directly in the UI to control how agents are routed, filtered, and managed. It brings that control closer to where teams already operate.

And importantly, the scope of what agents can do has expanded. What was once limited to investigations now includes mitigation actions as well, allowing teams to move from insight to action without changing context.

Finally, there’s a stronger focus on outcomes. With built-in executive summaries and efficiency metrics, teams can now understand the impact of these workflows in real terms, not just activity.

Taken together, this is a shift from a workflow-centric model to an experience that is guided, observable, and outcome-driven.

From investigation to resolution

Because the integration is two-way, everything stays connected. Findings from AWS DevOps Agent flow directly back into Dynatrace. Root cause, impacted services, and recommended fixes are all visible in a single place.

Teams no longer need to switch between tools or reconstruct the story themselves. The full path from detection to resolution is already laid out.

For teams running distributed systems on AWS, this changes daily operations. Instead of spending time figuring out where to look, teams can focus on fixing the issue and preventing it from happening again.

Bringing that context to developers with Kiro

This is where the story extends beyond operations. Kiro, AWS’s agentic development environment, brings that same production context directly into the developer workflow.

Instead of waiting for a handoff from operations, developers can access real production insights while they are building and fixing code. They can see exactly what failed, understand the root cause, and apply fixes with the same context that was used during investigation.

This removes one of the biggest sources of friction in software delivery. Developers are no longer dependent on separate teams to translate production issues. They are working from the same data in real time.

Now we have a closed loop: Dynatrace detects the issue. AWS DevOps Agent investigates it. Kiro brings that insight directly into the codebase where it can be resolved and improved.

That closes the gap between production and development in a way that was not possible before.

What this means for you

If you are running applications on AWS today, these developments can result in:

• Less time spent correlating data across tools
• Faster and more consistent incident resolution
• Fewer handoffs between operations and development
• Direct access to production context during development

Most importantly, it shortens the feedback loop. Issues are not just detected faster; they can be understood and resolved faster, and improvements can be applied to the code without delay.

Getting started

If you are already using Dynatrace on AWS, the next step is to connect these workflows.

Start by enabling the AWS DevOps Agent integration with Dynatrace to bring automated investigation into your environment. From there, extend that same production context into developer workflows with Kiro so your teams can act on insights directly.

This is the fastest way to move from insight to action and start seeing the value in day-to-day operations.

The post Moving from insight to action: How Dynatrace and AWS are reshaping cloud operations appeared first on Dynatrace news.

Organizations are evolving from human-driven operations to supervised autonomous operations, where AI investigates, recommends, and remediates, and humans stay in control of what matters most. A big part of delivering on that vision is working with the agents that customers already run in their cloud environments.

Harness the power of hyperscale agents

Each hyperscaler has AI agents that automatically investigate and help resolve production incidents using native cloud telemetry and tools. They act like embedded site reliability engineers, analyzing issues and recommending or executing remediation steps without waiting for a human to start the process.

AWS DevOps Agent provides investigation and remediation in AWS using native tooling. An Azure SRE Agent specializes in investigating and remediating Azure issues. And Google Gemini Cloud Assist is for incident analysis across Google Cloud Platform (GCP).

Over the past year, we’ve published how Dynatrace supercharges each of these cloud agents individually. When an issue occurs, Dynatrace Intelligence combines causal, predictive, and agentic AI using the Smartscape dependency graph to automatically link related symptoms and root causes across the environment into one unified problem card.

When Dynatrace integrates with the AWS DevOps Agent, dependency-aware root cause analysis combines with AWS frontier-agent capabilities, and joint customers report up to 70% reductions in mean time to resolution. When Azure SRE Agent connects with Dynatrace, deterministic, causation-based AI flows directly into Azure-native remediation workflows, cutting the back-and-forth between teams. And with Google Gemini Cloud Assist, Dynatrace delivers the same production context layer to GCP-hosted incidents: precise root cause, full topology, real business impact.

From integrations to intelligent orchestration

Many enterprises run workloads across AWS, Azure, and Google Cloud simultaneously, and managing three separate integrations with separate routing logic and separate cost controls is its own operational tax. Cloud SRE Agents provides a single orchestration layer that routes problems to specific hyperscaler agents based on configurable profiles to see everything happening across all three cloud agents.

The Cloud SRE Agents app writes findings back to Dynatrace, and provides your team with measurable visibility into autonomous actions.

How Cloud SRE Agents works

When Dynatrace Intelligence detects a problem and identifies the root cause, Cloud SRE Agents calls dedicated cloud-native agents from AWS, Azure, and Google Cloud to retrieve deeper insights from the sources that only they can reach: CloudTrail history, Azure subscription policy, GCP project IAM, recent deployments, and native runbooks. These agents run in parallel, gathering evidence as soon as the problem is detected. Their findings, and, where applicable, the recommended remediation path, are displayed in the same Dynatrace problem view that the on-call SRE is already using in their day-to-day workflow.

One view. No tab-switching. The work starts without you.

Three workflows do the orchestration in the background:

• Investigate evaluates your Interaction Profiles and dispatches matching problems to the right agents in parallel.
• Periodic Tasks polls each cloud provider for completion, detects stalled or timed-out investigations, and writes findings back as problem annotations.
• Event Handlers normalize the cloud-provider event stream so every action correlates back to its originating problem, end to end.

Cloud SRE Agents has the insights and intelligence to decide which agent gets which problem, tracks each run to completion, and brings the answers back together in a single view. The Overview tab provides a real-time, interactive network graph of problems, agents, and activities. The replay view allows the user to step back in time and get an overview of what has happened when, as well as the status of each investigation.

Intelligent routing with Interaction Profiles

In agentic operations, routing rules make the difference between turning autonomous systems loose on every alert and pointing them precisely where they earn their keep. Interaction Profiles are how you express routing judgment in Cloud SRE Agents. Each profile pairs a set of conditions with the agent or agents that should handle the problems flagged by the profile, and evaluates the conditions whenever Dynatrace Intelligence detects a problem.

The conditions you can write are deliberately broad. You can route by the cloud account, subscription, or project an incident touches; by problem category (availability, error, slowdown, resource contention); by affected entity type (a Kubernetes cluster, a database, a Lambda function); by tag, label, or any custom attribute carried in the problem record. Conditions combine with AND/OR logic and nest as deeply as you need, keeping real production routing policy inside the app rather than spilling into custom workflows or scripts.

Three ways teams put it to work

Route problems to the right cloud, automatically

A spike in Lambda error rates belongs to AWS DevOps Agent. An Azure App Service degradation calls for Azure SRE Agent. A Pub/Sub latency issue lands with Gemini Cloud Assist. In a multicloud estate, none of those decisions should fall to a human at 2:00 AM. A profile filtered by AWS Account ID, Azure Subscription ID, or GCP Project ID, then narrowed by resource type or tag, settles the routing question once. Every matching problem is automatically routed to the right specialist with the right cloud-native context.

Optimize spend with budget-aware routing

Cloud AI agents do work, and that work has a cost. Cloud SRE Agents lets you set a Monthly Duration Budget per agent and gate dispatch on it via a Has Available Budget filter: once the budget is exhausted, new investigations either stop (in strict enforcement mode) or proceed with a logged warning. The duration figure itself is a proxy, derived from Dynatrace event timestamps rather than the cloud provider’s clock, which makes it useful as a circuit breaker and directional signal, not a substitute for AWS, Azure, or GCP usage reports. The governance value is what matters: you decide how much autonomous investigation you’re willing to underwrite each month, and the system holds the line.

Tier autonomous investigation by problem type and entity

Not every Dynatrace problem warrants an autonomous investigation. Problem Category filters let you dispatch agents only to the problem categories that warrant it, for example, availability or error problems that require immediate action, rather than slowdowns or custom alerts where human triage might still be the right call. Layer on Entity Type filters, and you can further focus on specific infrastructure tiers (hosts, services, process groups, Kubernetes clusters). The result is a tiered model: high-severity issues receive immediate autonomous investigation, lower-severity signals queue for human review, and your team controls the threshold.

Governance that makes autonomous work measurable

Agentic operations earn trust when teams can see what the agents did, why, and whether it worked. Cloud SRE Agents treats that as a first-class concern, with two views built for the two audiences who care about it.

The Activity tab is the audit trail. Every investigation and mitigation appears as a card on a unified timeline; expand any card to see the agent’s full findings, the evidence it pulled, and the action it took or recommended. Each response can be rated Good, OK, or Bad, building a quality signal grounded in what your team actually saw rather than what the system predicted. When a single problem triggers work across multiple agents, those activities roll up to a single status (in progress, done, or stalled), so you always know where things stand without having to reconstruct the run from individual records.

The Statistics tab is where autonomous operations become a number you can show to a leadership team: problems handled, mitigations executed, average investigation time, MTTR and MTTI trends, success rates, and satisfaction scores broken down by agent. The same view doubles as a directional cost lens, since agent working time is the dominant driver on the cloud side of the bill. Treat the number as a trend signal and a circuit-breaker input, not a billing record (reconcile against AWS, Azure, and GCP usage reports for exact spend), and it makes the case for expanding agentic coverage with evidence rather than anecdote.

Why production context multiplies the value

What changes Cloud SRE Agents from a smart dispatcher into something more is what Dynatrace Intelligence contributes before an agent ever begins its analysis. Dynatrace delivers deterministic, causation-based root cause analysis grounded in Dynatrace’s Smartscape real-time dependency mapping, alongside business impact assessment and correlated telemetry. That context shapes the entire direction of the investigation. A cloud agent arriving with that foundation starts from “this specific service on this specific host is the root cause, and here’s the customer impact” rather than “something is wrong somewhere in this account.”

The numbers reflect it. According to AWS, organizations using the AWS DevOps Agent with Dynatrace see up to a 75% reduction in mean time to resolution.

Western Governors University, which runs a fully online learning environment for 200,000 students, uses AWS DevOps Agent with Dynatrace to automate cross-system correlation that previously required manual effort across multiple tools. At a larger scale, United Airlines transports more than 500,000 passengers daily across a hybrid environment that includes more than 500 AWS accounts, 20,000 Lambda functions, and 38,000 OneAgent deployments.

The team’s description of the before and after status is direct: previously, multiple tools with overlapping functions created gaps and black boxes during troubleshooting. With AWS DevOps Agent and Dynatrace, Dynatrace identifies the responsible layer, the agent investigates and provides resolution steps, and everything surfaces in a single Dynatrace view. No 3:00 AM tool-switching required.

Get started

For a closer look at the individual integrations, read the posts on AWS DevOps Agent and Dynatrace and Azure SRE Agent and Dynatrace, or see how Dynatrace Intelligence powers autonomous operations. To put your cloud agents to work today, install Cloud SRE Agents from the Dynatrace Hub. Cloud SRE Agents is currently available as a community-supported app.

The post Orchestrate multicloud AI agents for autonomous incident resolution appeared first on Dynatrace news.

What is the Kiro power for Dynatrace?

The Kiro power for Dynatrace delivers live observability data, root cause analysis, and remediation suggestions directly into the Kiro IDE, with no JSON editing or manual MCP setup.

Kiro is an AI-powered IDE that helps developers move from idea to working code through spec-driven development and an agentic assistant. To make the assistant genuinely useful in unfamiliar domains, Kiro recently introduced powers: curated, partner-validated bundles of MCP servers, steering files, and best practices that install with a single click and load on demand when a relevant task comes up. Install a power, and Kiro’s agent gains specialized expertise the moment you need it.

For Dynatrace customers already working in Kiro, it’s the shortest path yet from code to production insight. For developers new to Dynatrace, it’s a one-click way to ground Kiro’s reasoning in real facts from your environment, not guesses.

Why this matters for developers

Developers have historically been one step removed from production. When something breaks after deployment, the path to figuring out what went wrong usually runs through a Site Reliability Engineering (SRE) or operations team, and AI coding assistants can’t automatically and reliably remediate issues in software they’re unfamiliar with. Agents that can write code are guessing about how their code behaves in production unless they have access to real telemetry data.

The Dynatrace Kiro power for Dynatrace closes this gap through Dynatrace Intelligence, the agentic operations system at the core of the Dynatrace platform. Kiro’s answers are grounded in deterministic, causal AI and real-time production data, not probabilistic guesses.

With the tools provided by the Kiro power, developers can:

• Investigate live incidents and get root cause analysis directly in Kiro chat
• Query metrics, logs, and traces from production using natural language
• Surface security vulnerabilities affecting the code they’re working on
• Get remediation suggestions grounded in what’s actually happening in their environment

“Using Kiro powers for Dynatrace has been a total game-changer in the observability space. Deep-dive root cause analysis of complex system issues that once required lengthy manual intervention now happens in seconds, giving us unprecedented speed and confidence.”

Mike Kobush, Sr. Software Performance Engineer, NAIC

How to install the Kiro power for Dynatrace

Getting started takes only a few steps. Once installed, the Kiro power activates automatically when Kiro detects a relevant task. Mention an incident, a slow service, or anything that needs production context, and the Dynatrace tools and guidance will load in Kiro chat.

Prerequisites

• A Dynatrace account. If you don’t already have one, you can start a free 15-day trial.
• Kiro installed on your system.

Prepare the Dynatrace connection

First, create a Dynatrace Platform Token, which Kiro will use to authenticate. Then add the required permissions for the Dynatrace MCP server.

Install the Kiro power

The power can be installed from either the Kiro IDE or the Kiro powers website. For this walkthrough, we’ll use the IDE.

1. Launch the Kiro IDE.
2. Select the Ghosty icon with the lightning bolt to open the powers panel.
3. Select Dynatrace Observability from the Recommended
4. Select Install. The power is registered with placeholder values for the Dynatrace URL and token. Therefore, Kiro will show an error message that the MCP server can’t be reached.
5. To complete the configuration, select Open Settings and replace the placeholders with your environment details.

Configure your tenant and token

In the settings file, replace the two placeholders:

Start asking questions

Open a new chat in Kiro and start interacting with your Dynatrace environment using natural language. Query active problems or security vulnerabilities, request a root cause analysis to identify critical issues in production, or pull related logs and traces, all without leaving the IDE.

See it in action

The short demo below walks through installing the Kiro power for Dynatrace, verifying the connection, and running a first query against your environment to list the top 10 vulnerabilities detected by Dynatrace.

Get started with the Kiro power for Dynatrace

Kiro powers transform what used to be a stitching exercise (MCP servers here, steering files there, custom instructions somewhere else) into one single, ready-to-use bundle. The Kiro power for Dynatrace applies the same idea to observability: live production insight, causal root cause analysis, and remediation grounded in real telemetry, all available the moment a developer needs them.

The result is a tighter loop between writing code and understanding how it behaves in production. Less waiting for diagnostic data from someone else. Less guesswork from an AI assistant operating without context. And, more time spent on the work that actually matters.

Ready to try it? The Kiro Power for Dynatrace is publicly available: install it from kiro.dev or the Kiro IDE and start asking your environment questions.

The post Dynatrace observability is now a Kiro power appeared first on Dynatrace news.

Every developer knows the feeling: You’re in your IDE when something breaks. Error rates spike, alerts fire, and suddenly you’re out of the flow. Michael Kobush, Performance Engineer III at the National Association of Insurance Commissioners (NAIC®), wanted to eliminate the gap between development and runtime. Instead of switching tools or waiting on SRE support, NAIC set out to bring production insight directly into the developer workflow.

Let’s take a look at how NAIC embedded real-time observability directly into their development workflow and reduced investigation time to a few minutes.

The problem: Context switching kills developer productivity

Developers lose time the moment they leave their IDE, jumping between views of logs, metrics, and traces simply to understand what has changed.

For NAIC, this friction was slowing down their teams. Developers didn’t have access to production context, creating a dependency on SRE teams whenever investigations were needed. An analysis that should have taken minutes routinely took 45 minutes to an hour. Root-cause identification required manual correlation across multiple systems, a process that was neither scalable nor sustainable.

At Dynatrace Perform 2026, Kobush demonstrated how his team uses Kiro and Dynatrace at NAIC: Real-time observability in your IDE: How NAIC uses Kiro powers to drive developer productivity

The solution: Kiro powers and intelligent observability

Kiro is AWS’s agentic AI-powered IDE that takes a spec-driven approach to software development by turning natural language prompts into structured requirements, architecture designs, and implementation tasks to carry code from prototype to production.

NAIC installed the Dynatrace power for Kiro, one of Kiro’s installable powers that
dynamically connect domain-specific tools and context to the agent. Once connected, Kiro gives developers and AI agents access to Dynatrace data and insights, helping them pinpoint root causes and receive remediation recommendations directly in their workflow.

No switching between tools. No waiting on another team.

The aha moment: Root-cause analysis in minutes, not hours

The first prompt NAIC ran after connecting Kiro to Dynatrace set the tone for everything that followed. Kobush typed a single line into Kiro: “Tell me about problem P-18576.”

Within 30 seconds, Kiro returned a full problem summary with details and recommendations, pulling everything from Dynatrace automatically. Then, he pushed further: “Give me a really deep dive root-cause analysis of what happened.”

In under two minutes, Kiro returned a full root-cause analysis correlating telemetry, infrastructure signals, historical incidents, and the current problem from Dynatrace into a structured response that included:

• An executive summary
• Detailed problem context
• Infrastructure analysis
• Technical root-cause analysis
• Remediation strategies
• Conclusions and next steps

A preliminary assessment that would have previously taken 45 minutes to an hour was now done in minutes. More importantly, it wasn’t just faster; it gave the team a clear, connected view of how services, infrastructure, and dependencies contributed to the issue.

Beyond root cause: Automation across the entire workflow

What makes this more than just a faster diagnostic tool is how NAIC extended Kiro’s capabilities to automate the full incident response workflow.

Using Kiro’s steering files feature, NAIC configured Kiro to automatically generate a structured Markdown file whenever a root-cause analysis was completed. That file includes:

• Relevant DQL queries used during the investigation
• Direct links to the Dynatrace dashboards and data sources that surfaced the issue
• A clear summary of findings

With a Targetprocess MCP also connected, Kiro can take that analysis and populate a ticket directly, automatically loading all relevant context and sending it to the development team. For NAIC, this means the handoff from investigation to remediation is essentially hands-off. This level of automation doesn’t just save time; it creates consistent, repeatable workflows with built-in guardrails. Every incident gets the same structured, data-rich documentation, regardless of who’s investigating it or when.

This isn’t just about faster incident response. It changes how teams build and release software—giving developers immediate feedback on how their changes behave in real environments.

Proactive alerting: Catching problems before they crash

Root-cause analysis after the fact is valuable. With observability embedded directly into the workflow, teams can detect issues earlier in development and respond faster in production, closing the gap between building and operating software.

After noticing that a specific process had crashed, Kobush asked Kiro to set up an alerting profile that would trigger both before the crash, based on stress signals visible in the logs, and at the point of the crash. Kiro analyzed historical log data, identified pre-crash indicators, and built the alert profile automatically.

The result: NAIC’s team now receives early warning signals before a process fails, giving engineers time to intervene rather than react.

This shift from reactive to proactive operations is central to what the Dynatrace and AWS partnership enables. When observability data is embedded in the developer workflow rather than siloed in a separate platform, the entire engineering organization is better equipped to prevent incidents, not just resolve them.

Debugging a sneaky production bug

Perhaps the most telling story from NAIC’s experience with Kiro occurred during a routine error-rate investigation.

An application error rate had increased unexpectedly. Kobush asked Kiro to investigate. Two minutes later, Kiro identified the culprit: A developer had left debug code in the development environment, and it had made its way into production. Every time a user triggered that code path, it threw errors.

When Kobush sent the Markdown report to the developer, the response was immediate: “How did you find that? I’ve been looking for that.”

Kiro leveraged correlated logs, traces, systems context, and historical behavior from Dynatrace to pinpoint exactly where the issue originated.

Start embedding observability into your development workflow

NAIC’s experience highlights a broader shift: When developers, AI assistants, and systems all operate from the same runtime context, debugging becomes faster, releases become safer, and teams spend less time chasing issues and more time building.

The broader message from Kobush is simple: “I’m not a developer. I have a degree in biology and a minor in chemistry… But this, to me, is a game changer in the observability space. I can do things in seconds that would take me hours.”

The productivity gap between observability data and developer action is a solvable problem.

For DevOps engineers, SREs, and platform teams looking to accelerate incident resolution, reduce context switching, and move from reactive troubleshooting to proactive operations, the Dynatrace and Kiro integration offers a practical, immediately actionable path forward.

For developers, this means fewer interruptions, faster answers, and the ability to stay in flow, even when issues arise.

The post From reactive to proactive: How NAIC embedded AI‑powered observability directly into the IDE appeared first on Dynatrace news.

A new chapter of digital innovation is underway.

With the emergence of agent-driven services and generative capabilities, organizations now have new ways of building, operating, and improving digital products. These technologies open the door to richer customer experiences, faster feedback cycles, and continuous improvement powered by real-time insight.

At AWS Summits, Dynatrace is showcasing how our work with Amazon Web Services can help teams explore ways to turn agentic and generative innovation into measurable outcomes. Together, our teams have been delivering platform capabilities and deep integrations that allow customers to take advantage of these new AWS services at scale.

If you are attending an AWS Summit, stop by the Dynatrace booth to see agentic and generative innovation in action.

Turning new AWS capabilities into real-world outcomes

Dynatrace and AWS share a common mission to support customers as they innovate more efficiently and work toward better digital experiences across cloud-native and modern application environments.

Through close collaboration, Dynatrace has enabled customers to adopt new AWS services with confidence from the moment they become available. Dynatrace provides the intelligence layer that connects data across applications and infrastructure, allowing teams to better understand their user behaviors and application interactions, which can help inform business decisions and improvements.

As AWS introduces agent-based services and managed generative platforms, Dynatrace helps customers fully realize their value by connecting insight to action.

Bringing AWS-native signals into one view with Dynatrace Clouds app

A key part of unlocking this opportunity on AWS is access to the native signals that AWS services already produce.

Dynatrace Cloud Operations brings Amazon CloudWatch metrics and AWS service data points directly into the Dynatrace platform for a richer context. This allows teams to work with AWS-native telemetry alongside application behavior, user experience, and business signals in a single view.

With the Clouds app, customers can see CloudWatch metrics from AWS services in context with applications and workloads, connect AWS service signals using Dynatrace SmartScape® topology, and apply consistent analysis and automation across AWS native data and Dynatrace collected data.

At the AWS Summits, Dynatrace will demonstrate how the Clouds app enables shared visibility across AWS environments by connecting managed Dynatrace environments to Dynatrace SaaS and AWS services. This shared visibility becomes especially powerful when combined with agent-driven services and Amazon Bedrock, allowing agents and automated workflows to operate using trusted AWS data together with Dynatrace Intelligence.

See agentic and generative innovation in real‑world cloud environments

At the AWS Summits, Dynatrace will demonstrate how AWS services and Dynatrace Intelligence work together to enable a new way of building and evolving digital products. Live demos and hands-on conversations will show how insight flows naturally across teams and systems.

AWS DevOps Agent and continuous product improvement

With AWS DevOps Agent and Dynatrace, teams can understand in real time how changes impact users and business outcomes. Dynatrace provides trusted context from production environments that feed into agent-driven workflows, helping teams learn faster and deliver higher quality experiences with every release.

Amazon Bedrock and Bedrock AgentCore at real-world scale

With Amazon Bedrock and Bedrock AgentCore, teams are building agents that reason across information and act on behalf of users. Dynatrace enables customers to understand how these services behave in real-usage scenarios and scale generative capabilities within their products.

Kiro and Kiro Powers accelerating innovation from code to customer

Dynatrace connects live production insight back into agent-assisted development workflows, allowing teams to validate ideas using real usage and performance signals and shorten the cycle from idea to impact.

Dynatrace MCP connecting context across agent-driven systems

Dynatrace Model Context Protocol technology enables secure and scalable sharing of system context across agent-driven ecosystems. At the summit, see how MCP connects managed Dynatrace environments, Dynatrace SaaS, and Amazon Bedrock to support coordinated-intelligent behavior at cloud scale.

Amazon SageMaker and continuous product improvement

With Amazon SageMaker and Dynatrace, teams gain insight into how models perform in live environments and refine AI-driven features over time based on real-usage patterns.

Join us at the AWS Summits

Dynatrace and AWS have been working together to deliver the platform capabilities, integrations, and shared intelligence that support this opportunity in real‑world environments today.

We’ll be onsite at multiple AWS Summits across North America, EMEA, and APJ. Visit the Dynatrace booth to see live demos and connect with experts shaping the next generation of digital products. See us at an AWS Summit near you.

Dynatrace, Dynatrace SmartScape®, Dynatrace Model Context Protocol, Dynatrace MCP, and Dynatrace Intelligence are trademarks or registered trademarks of the Dynatrace, Inc. group of companies. Amazon Web Services, AWS, Amazon Bedrock, Amazon SageMaker, and CloudWatch are trademarks of Amazon.com, Inc. or its affiliates. All other trademarks are the property of their respective owners.

The post Unlocking a new era of digital innovation with Dynatrace and AWS appeared first on Dynatrace news.

Unify cloud resources across accounts, regions, and services into a single, real-time dependency graph

As workloads continue to sprawl across AWS, Azure, Google Cloud, and on-premises data centers, teams are overwhelmed by massive volumes of telemetry and constant change. Simple questions like “What service depends on this?” or “Is this vulnerability exposed?” often turn into hours of manual investigation. Smartscape changes this dynamic by unifying every cloud asset, metadata field, and connectivity path into a single, real-time dependency graph, delivering instant answers and visualizing them in a continuously updated Smartscape view. Instead of hopping between AWS and Azure consoles, platform teams finally get a continuously updated picture of how their cloud environments are truly behaving.

With new AWS integrations, Smartscape now also captures deep configuration data, such as VPCs, load balancers, security groups, subnets, network services, and compute metadata, and models these dependencies as native cloud entities. Unlike any other observability vendor, Dynatrace provides full access to the raw observability data in Grail via Dynatrace Query Language (DQL), unlocking powerful exploratory analytics use cases. Each entity includes the complete unprocessed definition of the cloud service as JSON, covering metadata, resources, configuration, security, and networking details, and tags, making this information fully transparent and directly queryable. This unified model delivers immediate customer value:

• Security posture and exposure analysis: detect publicly reachable endpoints, analyze real security group and network policy paths, and prioritize fixes based on true blast radius and reachability.
• IAM hygiene and drift control: uncover risky role sharing across Lambdas, identify configuration drift across accounts and regions, and validate whether access paths reflect intended policy.
• Cost optimization: identify x86 vs ARM workloads, right-size EC2, RDS, and EBS based on real utilization, and connect cloud spend to actual service dependencies to make safer cost decisions.
• Architecture & multi-account visibility: map cross-VPC and cross-region dependencies, unify runtime topology across all cloud accounts, and eliminate hidden or forgotten resources.
• Operational readiness & risk reduction: understand how misconfigurations or outages propagate through infrastructure and into applications, improving impact assessment and response.

The Clouds app provides comprehensive insights and metadata, including metrics and logs for your services, deep insights into resource configurations and cloud topology, and the ability to leverage your cloud tags for access and visibility. With Clouds, teams can interactively explore and analyze their cloud estate, apply segment filters, follow connectivity paths, and compare environments.

Understand your entire setup at a glance through advanced visual analytics

The new Smartscape app’s domain-specific views turn complex, multi-layered cloud estates into something teams can understand instantly. Visual exploration makes it easier to:

• Understand real, observed connectivity between workloads across VPCs and environments, enriched with cloud networking context such as subnets and security constructs.

• Instantly understand problems and their blast radius with affected entities clearly highlighted.

• Identify hidden relationships or unintended dependencies that spreadsheets or lists will never surface.
• Validate migration plans, architectural assumptions, and segmentation strategies before changes go live.

Create a single source of production truth with flexible views and segmentation across cloud dimensions, including tags, accounts, regions, environments, and ownership.

This visual context is often where the “aha” moments happen, the point where teams finally see how their cloud is structured, where risks live, and where optimizations will have the greatest impact.

Utilize DQL for advanced insights customized and enriched with what matters to you

For deeper investigation or automation, DQL lets teams query relationships, join topology with logs and metrics, and run impact assessments programmatically. These queries can be operationalized through dashboards and notebooks. Learn more about how to utilize the new Smartscape DQL commands to query the AWS topology.

Kubernetes: how Smartscape gives you clarity on fast-moving, complex clusters

Kubernetes environments evolve continuously: pods appear and disappear within seconds, configurations drift, and a single missing reference in a YAML file can cascade into service failures across namespaces, or even clusters. While traditional tools expose fragments of this reality, they fall short when teams need complete answers to foundational questions like what does this depend on?, what changed?, or why did this break?

Smartscape further enhances Dynatrace Kubernetes observability by unifying Kubernetes objects, relationships, and configurations across clusters and clouds into a single, real‑time dependency graph. Instead of jumping between kubectl commands, point‑in‑time UIs, and disconnected dashboards, teams gain a continuously updated, system‑level view of how their Kubernetes environments actually behave.

With enhanced ingest, Smartscape now captures all major Kubernetes object types, including ConfigMaps, Secrets, Ingress, PV/PVC, workloads, services, and namespaces, and stores their full YAML definitions and metadata directly in Grail. Teams can query configurations across clusters and clouds, trace live end-to-end dependency paths, and automatically surface misconfigurations, missing references, policy violations, and drift. What was previously scattered across files and tools becomes instantly explorable context, at a global scale. The value of Smartscape can be felt immediately:

• Faster troubleshooting: trace live relationships across clusters, namespaces, workloads, and services to pinpoint drift or misconfigurations that cause runtime failures.
• YAML misconfiguration detection: identify missing references, invalid fields, or policy violations with full YAML-in-context, and regenerate correct configurations using Dynatrace Intelligence.
• Ephemeral awareness: retain visibility into short-lived workload changes or crashes that normally disappear before engineers can inspect them.
• Policy and compliance enforcement: check networking, storage, config maps, resource quotas, and image standards at the object level for stronger governance.
• Safer releases: segment clusters by team or namespace and visualize impact paths before and after deployments to reduce risk and improve deployment confidence.

All enhanced Kubernetes insights and YAML definitions are directly accessible within the Kubernetes app.

In Smartscape, access the Kubernetes domain view, where you can:

• Visualize cluster topology for instant clarity on structure and relationships.
• Follow real dependency chains across namespaces, workloads, services, and underlying infrastructure to understand impact paths.
• Segment clusters dynamically by team, namespace, environment, or workload identity for precise context.
• Isolate critical workloads or namespaces for focused investigation and remediation.
• Validate architectural assumptions by comparing expected versus actual relationships.

For advanced analytics, DQL lets you query Kubernetes objects, relationships, and signals at scale. For actual use cases and examples, check out this notebook on the Dynatrace Playground.

Other domain-specific enhancements, from infrastructure to services

The new Smartscape unlocks a broader range of high-impact use cases across every layer of your IT environment, with topology-enriched information across apps; many new ways to explore your data via DQL, and several additional, use-case-optimized Smartscape views. Below are some additional examples and inspiration to help you get started:

Services

Smartscape now gives you deeper insight into how services connect and communicate in real time. By modeling upstream and downstream dependencies alongside KPIs and infrastructure anchors, Smartscape makes it easier than ever to understand how services interact, where failures originate, and how changes ripple across the stack.

With the new Service Dependency Graph view, teams can instantly visualize their service landscape. The interactive graph makes it easy to follow call flows, isolate a single service and its direct dependencies, highlight performance or error hotspots, and identify unexpected communication paths. Apply your own business context, for example, ownership, to help teams see how services come together to deliver business functionality.

Infrastructure

Smartscape expands visibility into infrastructure by mapping all running components, showing how they’re connected, and identifying how performance issues might impact other critical services. The Infrastructure Overview turns this into an intuitive, navigable map that lets teams focus on the data relevant to them and spot bottlenecks or drift patterns through topology shape. Building on this foundation, upcoming Dynatrace enhancements will allow teams to visually inspect host‑to‑process chains and explore network paths enriched with SNMP/LLDP data.

Problems

Smartscape enhances problem analysis by automatically connecting detected anomalies to the entities and dependencies they impact across your environment. This shows not only what is broken, but how issues propagate across services, workloads, and infrastructure, giving teams immediate clarity on root cause and blast radius. The Problem Graph highlights affected entities, correlates related anomalies, allows for impact isolation, and provides AI-powered insights in context.

End-to-end discovery

The Smartscape app also exposes your entire digital ecosystem as one coherent model, visualizing all dependencies and connecting cloud resources, Kubernetes clusters, infrastructure components, and services end-to-end in the Smartscape on Grail view. This allows teams to understand the real system structure, uncover hidden dependencies, and validate architectural assumptions with complete context rather than piecemeal data.

Experience the new Smartscape today

Smartscape changes how teams operate by providing automatic, real-time context across all domains, enabling faster troubleshooting, safer releases, stronger security posture, and more cost-efficient operations.

• Explore domain-specific views for AWS EC2, Kubernetes, Infrastructure, and Services, with Azure coming soon.
• Run impact analysis with DQL graph queries.
• Combine topology with logs/metrics/traces/RUM for full stack insights.
• Let Dynatrace Intelligence take safe, informed actions based on production truth.

The post The new Dynatrace Smartscape improves operational efficiency across clouds, Kubernetes, infrastructure, and more appeared first on Dynatrace news.

Dynatrace has surpassed $1 billion in lifetime AWS Marketplace sales and earned the AWS Financial Services Competency—a milestone that reflects expanded co-innovation, deepened AI-powered observability capabilities, sustained triple-digit growth over the past three years, and accelerated customer adoption across global markets.

These achievements build on the advancements announced at AWS re:Invent 2025, including attaining the AWS Agentic AI Specialization, expanding integrations with Amazon Bedrock AgentCore and AWS DevOps Agent, and being named AWS Public Sector Technology Partner of the Year for LATAM.

Here is a closer look at how these milestones advance agentic AI observability and cloud operations for customers on a global scale.

The power of a strategic collaboration that puts customers first

Reaching $1 billion in sales on AWS Marketplace is a testament to the powerful synergy between Dynatrace and AWS. Dynatrace growth in AWS Marketplace sales has surged over the past three years, delivering sustained triple-digit growth.

As enterprises adopt cloud-native procurement to accelerate modernization, demand for Dynatrace continues to expand globally. AWS Marketplace has been instrumental in supporting this growth, enabling Dynatrace to reach customers across industries in over 20 regions through streamlined tax handling, multi-currency support, and other strategic offerings.

“Collaborating with Dynatrace through AWS Marketplace has been transformative for our joint customers,” said Ed Smoke, Vice President of Intelligent Operations Partner Alliances at AHEAD. “The streamlined procurement process and seamless integration of the Dynatrace AI‑powered observability platform with AWS services enable organizations to accelerate modernization with confidence. By leveraging AWS Marketplace, we’ve helped customers align their investments with AWS Enterprise Discount Programs, delivering greater value and efficiency.”

Global scale meets local impact

Enterprises continue to accelerate Dynatrace adoption through AWS Marketplace to streamline procurement, reduce onboarding time, and align spending with AWS Private Pricing Addendum. Customers trust the Dynatrace platform to:

• Simplify procurement, streamlining the buying process to get technology into the hands of teams faster.
• Optimize cloud spend, utilizing AWS committed spend to invest in observability that drives efficiency.
• Scale confidently, deploying Dynatrace across complex, multi-region AWS environments with ease.

These organizations aren’t just buying software; they are investing in a platform that serves as the foundation for their digital resilience. And this streamlined route allows customers to adopt the Dynatrace AI-powered observability platform quickly while maximizing the value of their AWS investments.

“Partnering with Dynatrace through AWS Marketplace has been a strategic win for Storio group,” said Alex Hibbitt, Engineering Director, Customer Platform at Storio group. “The streamlined procurement process, providing efficiency for both our teams and our vendors, has allowed us to quickly adopt the Dynatrace AI-powered observability platform to gain real-time insights across our cloud environments. By centralizing our purchasing through AWS Marketplace, we were able to align our spend with our commitment to AWS, maximizing the value of our cloud investments while accelerating our modernization journey. The Dynatrace platform’s seamless integration with AWS services has empowered us to innovate faster, reduce operational complexity, and focus on delivering exceptional value to our customers.”

Agentic AI is fueling the next wave of innovation

While generative AI remains wildly popular, agentic AI—systems that don’t just generate content but take action—is moving to the fore. In fact, according to the recent Dynatrace research report, The Pulse of Agentic AI, 50% of agentic AI projects are in production for limited uses or departments, and 23% are in mature, enterprise-wide integration. Further, 72% of respondents expect agentic AI budgets to increase in the next year.

Dynatrace recently earned the AWS Agentic AI Specialization—a distinction that validates our deep technical expertise in observing and governing agentic AI systems.
As organizations move from AI experimentation to production, they face new challenges, including how to monitor an AI agent that acts autonomously and how to ensure it stays within its guardrails.

Our expanded collaboration with AWS directly addresses these needs through integrations designed to provide end-to-end visibility into an AI ecosystem.

1. Amazon Bedrock AgentCore Observability

Dynatrace continues to expand deep technical integrations with AWS to support modern cloud‑native and AI‑driven architectures. Dynatrace provides full‑stack analytics across services such as Amazon Bedrock AgentCore and AWS automation pipelines, enabling teams to operate, secure, and scale agentic AI workloads with confidence.

To build agents on Amazon Bedrock, teams need more than just logs—they need context. This new integration provides native, end-to-end observability for Amazon Bedrock AgentCore, enabling developers and site reliability engineers to:

• Monitor agent interactions across various AWS offerings.
• Debug complex workflows by tracing requests from the user to the LLM and back.
• Audit performance to ensure agents deliver accurate, safe, and efficient results.

2. Kiro powers and Kiro Autonomous Agent

Dynatrace is also integrating with Kiro, AWS’s agentic integrated development environment. Kiro leverages deep insights from the Dynatrace AI-powered observability platform to accelerate developer productivity.

Imagine an AI agent that can handle bug triage, suggest code fixes, or even implement features—all while being guided by the precise telemetry data from Dynatrace. This Kiro Powers integration extends observability directly into the developer workflow, enabling:

• Faster root-cause analysis. Agents can autonomously troubleshoot issues based on real-time performance data.
• Spec-driven development. Actionable insights help developers build higher-quality code from the start.

3. AWS DevOps Agent

To further streamline operations, Dynatrace has integrated with the AWS DevOps Agent. This collaboration accelerates root-cause isolation by adding domain-specific AWS context to Dynatrace findings.

The result is autonomous troubleshooting that detects performance degradations, quantifies their business impact, and provides clear remediation instructions. It’s about reducing the noise to let teams focus on solving the problems that matter most.

“By leveraging AWS Marketplace, we were able to align our spend with our AWS Enterprise Discount Program, maximizing the value of our cloud investments while accelerating our modernization journey.” said Luca Domenella from Soldo.

Accelerate your cloud journey with Dynatrace and AWS

Reaching $1 billion on AWS Marketplace is a historic moment for Dynatrace, but it’s just the beginning. The combination of triple-digit growth in AWS Marketplace sales over the past three years, expanded deal sizes, and groundbreaking innovation in agentic AI, Dynatrace is moving faster than ever.

Whether you are looking to simplify your cloud operations, secure your AI workloads, or simply get more value from your AWS investment, Dynatrace is the partner you need. Check out our AWS Marketplace listing to see how easy it is to get started, and contact us today for more information.

The post Dynatrace accelerates a new era of growth and innovation with AWS appeared first on Dynatrace news.

Enterprise cloud has outpaced traditional reactive monitoring. As workloads multiply across AWS, Azure, and Google Cloud—and extend into data centers—teams find themselves contending with vast amounts of telemetry, ephemeral infrastructures, and constant change. Visibility is tough; clarity of impact, ownership, and root cause is tougher.

Today’s tech stacks are distributed by design, featuring containers, serverless architectures, managed services, and data planes that can spin up and down in seconds. High-cardinality signals, fragmented logs, and partial traces create blind spots across accounts, subscriptions, and projects. In hybrid setups, network overlays, identity boundaries, and platform services blur the lines between where issues begin and how they propagate.

Organizationally, the challenge is even bigger. Platform, SRE, Dev, SecOps, and FinOps each hold a piece of the truth. However, inconsistent tagging and governance, manual runbooks, and handoffs across time zones can slow MTTR, fuel tool sprawl, and inflate budgets. The goal is to provide clear risk and cost insights, reduce noise, and deliver faster, context-rich answers.

However, visibility alone isn’t enough to achieve this goal. AI-driven context, such as topology-aware analytics, causal correlation, and safe automation, shifts operations from reactive firefighting to proactive prevention that’s aligned with SLOs, compliance, and budgets.

Today, we’re introducing Dynatrace enhanced cloud operations for AWS, Azure, and Google Cloud, delivering complete visibility across all your cloud and hybrid environments with deeper, actionable insights. If you’re ready to take the guesswork out of monitoring your cloud environments, read on to see how Dynatrace helps you collaborate faster, resolve issues earlier, and run at enterprise scale with confidence.

What is Dynatrace Cloud Platform Operations?

Dynatrace Cloud Platform Operations takes the guesswork out of monitoring by redefining how organizations manage complex cloud environments. By unifying all cloud signals—metrics, logs, and events—into a single AI-powered platform, Dynatrace delivers complete visibility and actionable insights at scale.

Every data point is enriched with context and analyzed by Dynatrace AI, enabling proactive automation and informed decision-making. This ensures faster troubleshooting, better resource efficiency, and simplified onboarding—all without the need for additional infrastructure components.

Organizations eliminate blind spots and accelerate resolution by gaining real-time visibility into the health, performance, and configuration of their cloud resources. This allows them to maintain the correct governance through tag-based control, ownership, and cost allocation, thereby aligning teams while reducing operational noise.

Additionally, the latest advancements in cloud observability provide real-time visibility into resource health, performance, and configurations, allowing teams to act quickly and decisively while reducing the time to resolution.

Start monitoring your cloud environment

Spin up monitoring without spinning up your infrastructure. The new cloud connections are fully managed by Dynatrace and guided by a simple wizard. It gets you from setup to actionable telemetry in minutes; no agents to wrangle, no custom pipelines to maintain.

Once connected, you gain instant visibility across all your cloud environments. Connect your AWS, Azure, or GCP accounts once, and Dynatrace auto‑ingests everything from compute and databases to storage, networking, and security configurations. Platform coverage has been expanded to capture more data types than ever, including metrics for any supported cloud service and a richer set of cloud events, such as hyperscaler‑native security alerts.

The new cloud connections are:

• Secure by design: native authentication offers least‑privilege access and auditable permissions.
• Practitioner‑friendly: a step‑by‑step wizard with built‑in checks and defaults works across accounts and regions.
• Zero overhead: the Dynatrace platform manages the connection end‑to‑end, so you focus on insights, not maintenance.

With all your cloud data unified, Dynatrace automatically applies the tags you already use in your cloud environments, bringing your existing operational model directly into the platform. Your existing cloud tags instantly drive access control, ownership, cost allocation, alert routing, and preventive workflows, with no manual tagging or re‑mapping required. Additionally, tag‑enriched signals keep operations aligned by allowing you to filter everything by owner, app, or environment for precise alert routing and actionable insights. This paves the way for more advanced analytics and clear insight into what’s happening across every environment.

The enhanced cloud ingest not only unifies telemetry and context; it also captures all configuration details (VPCs, load balancers, security groups, subnets, network services, compute metadata, and more). The new Smartscape® uses this information, along with cloud monitoring data, to provide a comprehensive, always-accurate topology of your cloud infrastructure. You can use the new Smartscape app to navigate your cloud topology, visualize dependencies, and eliminate hidden or forgotten resources. Ready-made views for AWS and Azure provide a comprehensive, automatically discovered cloud inventory across services, databases, networking layers, and security controls.

Additionally, you get ready-made dashboards, essential metrics, and expanded cross-cloud visibility, providing you with immediate clarity, especially when issues originate on the provider side, thanks to built-in AWS Health event integration.

All of this comes together in our newly enhanced Clouds app, a shared place for teams to analyze services, metrics, events, and logs. Pre-built dashboards and alerts automatically highlight unhealthy resources, helping teams stay ahead of issues, while the streamlined onboarding process eliminates the need for additional collectors or components. It’s fast, safe, and modern—exactly how cloud onboarding should feel. Ready to see it in action? Keep reading to see the magic.

Transform from reactive monitoring to proactive cloud operations

Dynatrace transformed cloud monitoring into proactive cloud operations by combining AI-powered observability with intelligent automation. This allows organizations to move beyond simply identifying problems to actively solving and preventing problems.

With Dynatrace, you can remediate issues before they impact your users, prevent future issues, and optimize your cloud environments to ensure they operate at maximum efficiency and resiliency.

Prevention

Leave reactive firefighting behind and gain the foresight needed to stay ahead of issues. Dynatrace’s AI-driven automation delivers proactive insights that allow you to identify and resolve potential problems before they impact your users. By predicting anomalies and triggering automated workflows, Dynatrace helps maintain high availability and optimal performance across your cloud environments. This proactive approach minimizes downtime, protects user experience, and ensures your teams can focus on strategic initiatives rather than crisis management.

Remediation

Instead of wasting valuable time on lengthy resolution cycles that disrupt business operations, accelerate recovery with intelligent automation. Dynatrace automates root cause analysis and remediation, enabling self-healing workflows that dramatically reduce resolution times. By eliminating manual troubleshooting and streamlining incident response, your teams can focus on driving innovation rather than firefighting. With AI-driven insights and automated corrective actions, Dynatrace ensures issues are resolved quickly and efficiently, thus minimizing impact, improving reliability, and keeping your business moving forward.

Optimization

Stop overspending on infrastructure due to a lack of visibility into resource usage and performance. With Dynatrace, you can continuously optimize both cost and efficiency across your environment. Real-time insights into resource consumption and application performance allow you to identify waste and prevent unnecessary expenses. By leveraging AI-driven analytics and automated recommendations, you can improve cost management and drive peak performance in your applications.

Ready to experience the new world of Cloud Operations for yourself?

With the introduction of enhanced cloud operations for AWS, Azure, and GCP, you can achieve smarter collaboration, faster issue resolution, and streamlined operations at scale using the Dynatrace Cloud Platform Operations solution.

AWS enhanced cloud operations

Discover how easy it is to get started using proactive monitoring capabilities for AWS. AWS cloud operations are now generally available for all Dynatrace SaaS customers.

Azure enhanced cloud operations

Interested in seeing the magic for your Azure environments?
Join the Azure Preview program

GCP enhanced cloud operations

Are you a GCP customer looking for a cloud operations solution?
Join the GCP Preview program

The post Redefining cloud operations: Dynatrace brings intelligence to observability appeared first on Dynatrace news.

March 31, 2026 update

Today we congratulate AWS on the general availability of AWS DevOps Agent. This marks an important step forward in how teams operate and innovate in the cloud, moving closer to systems that can investigate and respond with minimal human intervention.

At Dynatrace, we are proud to have collaborated with AWS on this initiative from the beginning. Together, we have worked to bring observability, AI, and automation closer together to help customers simplify operations and resolve incidents faster.
Our joint customers are already seeing measurable value, including up to a 70 percent reduction in mean time to resolution, as teams move from reactive troubleshooting to more intelligent and automated workflows.

This work reflects a broader shift toward agentic operations. We are continuing to deepen our collaboration with AWS across DevOps Agent and other AI services as this space evolves. This foundation sets the stage for how the Dynatrace platform and AWS DevOps Agent integration works in practice.

How AWS DevOps Agent and Dynatrace complement each other to resolve incidents faster

It’s late at night, you’re on call, and an alert fires for an AWS application. You need to assess the severity, understand the impact, and quickly notify the relevant teams. Until now, that potentially meant toggling between Dynatrace and the AWS Console to piece together the full picture. With the AWS DevOps Agent and Dynatrace integration, you instantly have all the information you need at every stage of remediation.

AWS DevOps Agent represents a new class of frontier agents: AI that works autonomously for hours or days, investigating incidents without constant human intervention. Dynatrace provides causal and predictive AI that pinpoints the root cause of issues and anticipates problems before they escalate. Together, they create something neither can deliver alone: end-to-end incident resolution that spans from early warning through root cause to remediation.

When AWS announced the DevOps Agent at re:Invent last December, they showcased this integration as a key use case, demonstrating how autonomous investigation becomes dramatically more effective when powered by Dynatrace precise, topology-aware production context. The agent doesn’t just correlate signals; it understands what those signals mean for your business.

Experience topology-aware root cause analysis with guided mitigation

Consider a typical CRM stack: a React frontend on S3 and CloudFront, an ALB routing to Lambda-hosted Python services implementing the CRM business logic, backed by an RDS PostgreSQL. During normal operations, the responses take ~1 ms, but suddenly those degrade to 1 s+. Dynatrace instantly detects the problem with all relevant context, including business impact, and automatically triggers the AWS DevOps Agent to initiate further investigation.

Figure 1: Dynatrace and the AWS DevOps Agent work hand in hand to analyze and mitigate the problem.

• Root cause analysis with causal AI: Dynatrace detects response-time degradation and automatically gathers relevant context, including potentially AWS resources causing the issue, such as Lambda, RDS, and ALB.
• Seamless collaboration with AWS DevOps Agent: Dynatrace triggers the AWS DevOps Agent and passes full runtime context, allowing it to trace the execution path from symptom to failing component.
• Pinpoint the root cause: The AWS DevOps Agent analyzes underlying RDS logs, identifies DROP INDEX commands that correlate with slowdown events, and surfaces the findings directly in Dynatrace, without tool switching. The commands are traced to an input error by a database administrator.
• Recommend and stage a fix: the agent provides a clear diagnosis, recommended remediation steps, and proposed action that’s ready for human approval.
• Prevent recurrence: The agent suggests proactive monitoring of database logs for similar commands to prevent future incidents.

This always-on, on-call workflow accelerates triage, allows topology-aware root cause analysis, guides mitigation, and adds preventative recommendations. It works across a broad set of AWS services, including AWS Lambda function errors, Amazon EKS container failures, Amazon VPC connectivity issues, and more.

Ready to try it out yourself?

With the Dynatrace integration into AWS DevOps agents, you get:

• Fewer handoffs and clearer ownership with a single investigation narrative (no bouncing between teams/tools)
• Less manual correlation as Dynatrace supplies topology, dependencies, and traces as a ready-to-use production context
• Faster “why” analysis as AWS DevOps Agent correlates AWS telemetry with change/deployment history and proposes mitigations
• A more repeatable incident response, including prevention recommendations to reduce repeats

For more details on the preview and how to try it yourself, have a look at this hands-on walkthrough on the AWS Cloud Operations Blog. You can also refer to AWS documentation for instructions on connecting Dynatrace and the AWS DevOps Agent.

For more news on Dynatrace and AWS, have a look at this recent blog post.

The post Integration with AWS DevOps Agent: Autonomous investigations powered by production context appeared first on Dynatrace news.

At Dynatrace, we believe you shouldn’t have to fix every cloud misconfiguration—rather, we believe you should focus on the ones that matter to your production environments. That’s why we’ve integrated Dynatrace with AWS Security Hub and Kiro CLI to streamline triaging and remediation of critical findings, focusing efforts where they count most.

The challenge in managing complex cloud environments

Modern cloud environments are complex with many moving parts and constant change. Whether initially misconfigured or affected by configuration drift, these environments pose a compliance issue and a significant security threat if exploited by malicious actors. Depending on the scale and composition of your environments, you may end up with thousands of misconfigurations that need to be addressed. Or do they?

AWS provides native services, such as AWS Security Hub, that help detect misconfigurations in your cloud environments. Such misconfigurations are often referred to as compliance findings. Many of the findings might not pose an immediate threat to production systems. AWS Security Hub’s latest capabilities, such as Exposure findings, already help a lot in distilling the most critical findings.

Is it possible to get additional validation of findings with deeper context into your production services and applications? That would help you understand which findings affect your crown jewels and prioritize their remediation over other issues.
And is it possible to automate this process, thereby reducing human intervention and the required deep understanding of the complex environments?

This is a modern dilemma for SREs and security teams: how to prioritize remediation without compromising operational integrity or wasting valuable resources on non-critical issues.

Context-aware remediation with Dynatrace and Kiro CLI

The Dynatrace® AI-powered observability platform monitors your applications and has all the runtime insights required to help organizations navigate through thousands of compliance findings and use the runtime context to prioritize them. Dynatrace’s Model Context Protocol (MCP) server exposes this deep runtime context to automation and agentic AI-driven workflows.

The Kiro CLI allows interaction with specialized AI agents and AWS native capabilities using MCP servers. It serves as the SRE assistant, possessing knowledge of all connected products and the ability to remediate issues.

To further streamline and connect the dots, Dynatrace integrates with AWS Security Hub, supporting the latest OCSF (Open Cybersecurity Schema Format). This allows SREs to fine-tune the triaging and enrichment to their particular needs, leveraging Dynatrace Workflows as the automation engine.

In the next section, we present a use case with two scenarios that demonstrate how detection, triage, and remediation of cloud misconfiguration were made more efficient using Dynatrace and the latest AWS advances in automation and AI domains.

Intelligent triage in action

This use case starts with compliance findings, detected by AWS Security Hub. In the first scenario, an SRE retrieves critical findings using the Kiro CLI and verifies them with Dynatrace using the Dynatrace remote MCP server, which is connected to a dedicated Kiro custom agent.

In the second scenario, the initial validation is automated with Dynatrace Workflows and supported by the AWS Security Hub integration. The validated findings are then sent to a Jira ticket, which can be picked up later by the SRE directly from the Kiro CLI.

By combining Dynatrace deep observability with Kiro CLI intelligent automation, teams gain the ability to triage findings based on actual runtime impact. Instead of treating every misconfiguration as equally urgent, this solution helps prioritize those misconfigurations that pose real risks to business-critical applications. This not only reduces alert fatigue but also ensures that remediation efforts are focused, efficient, and aligned with operational priorities.

The result? A streamlined security posture that’s proactive rather than reactive. Teams can move faster, reduce manual overhead, and maintain a higher level of confidence in their cloud infrastructure while keeping production safe and stable.

Scenario 1: Kiro CLI-driven validation with Dynatrace MCP

In this scenario, an SRE interacts with Kiro CLI to perform validation and remediation actions with the help of Dynatrace MCP.

1. Detection: AWS Security Hub identifies cloud misconfigurations.
2. Verification
   • An SRE interacts with the Kiro CLI to extract top findings.
   • The Dynatrace agent, invoked by the Kiro CLI, utilizes the Dynatrace MCP server to verify whether these findings impact production applications.
3. Remediation
   • SRE remediates confirmed findings via Kiro CLI; others are suppressed.
   • Findings are resolved and verified in AWS Security Hub.

Scenario 2: Dynatrace-driven automated triaging and Kiro CLI remediation

In this scenario, initial validation and triage are automated in Dynatrace Workflows, and the SRE then acts on the Jira tickets to perform assisted remediation using the Kiro CLI.

1. Detection: AWS Security Hub identifies cloud misconfigurations.
2. Verification
   • AWS Security Hub integration ingests findings into Dynatrace.
   • A Dynatrace workflow analyzes new critical/high-risk findings and performs the automated verification using the Davis CoPilot® workflow action.
3. Remediation:
   • A Jira ticket is created automatically with the summary of the verification results.
   • An SRE uses Kiro CLI to act on the Jira findings. Confirmed findings are remediated, while the unconfirmed findings can be suppressed.
   • Findings are resolved in AWS Security Hub and are no longer in the top findings dashboard in Dynatrace.

What’s next

As AI footprint growth and capabilities evolve, the collaboration between AWS and Dynatrace will extend to additional use cases for agentic AI workflows and observability-context-supported issue remediation. One such example is covered in this recent blog post.

In this blog post, we demonstrated how the Dynatrace remote MCP server can be integrated with Kiro CLI for SRE use cases. Stay tuned as the collaboration extends to additional use cases that help development teams remediate vulnerabilities and performance issues directly from their IDEs.

Get started

To get started, learn more about Dynatrace MCP and sign up for the preview to experience how real-time production context makes your organization more efficient.

For details on how to set this up, refer to Dynatrace documentation.

The post Smarter cloud security with Dynatrace and Kiro CLI appeared first on Dynatrace news.

Editor’s note

At AWS re:Invent, innovation isn’t just a topic of conversation — it’s on full display for the world to see. As we prepare for the latest innovations from the expo floor in Las Vegas, the message is clear: The future is AI-driven, and the foundation for success is built on intelligent observability.

This presents a unique opportunity, and Dynatrace with Amazon Web Services provides a clear path forward. With AI-driven automation and real-time observability at the core, we help organizations reduce risk, resolve issues faster, and optimize cloud investments.

This guide explores the key themes of AWS re:Invent and highlights how Dynatrace and AWS empower organizations to build, modernize, and secure their cloud environments with confidence. It’s time to accelerate and lead the way.

— Jay Snyder, SVP of Global Partners and Alliances, Dynatrace

The latest news and announcements from AWS re:Invent

The Dynatrace AI-powered observability platform is purpose-built to tame the complexity introduced by new generative and agentic AI initiatives and the explosion of data that teams must manage. By integrating with new agentic-focused services from Amazon Web Services, Dynatrace provides automated, intelligent observability and security that organizations need to innovate faster and more securely. Check out the latest news and integrations, including the following:

• AWS DevOps Agent — an autonomous AI agent that resolves and proactively prevents incidents, while continuously improving reliability and performance — has reached general availability. Dynatrace has collaborated with AWS on this initiative from the beginning. AWS DevOps Agent works with Dynatrace production context to reduce operational toil, identify recurring issues, and strengthen application reliability across AWS environments.
• Our new Modern Cloud Operations for AWS feature enables automatic discovery of new AWS services, native telemetry and metadata ingestion for seamless observability, and unified dashboards with AI-driven insights for performance, cost control, and modernization.
• Dynatrace is now integrated with Kiro, AWS’ agentic integrated development environment.
• Customers leveraging agentic systems built on AWS services like Bedrock AgentCore can get visibility into their interactions across AWS services, enabling developers to monitor, debug, optimize, and audit agentic workflows with Dynatrace’s support for Amazon Bedrock AgentCore Observability.
• Teams can perform cloud security posture reviews and receive real-time observability and AI-driven insights, accelerating threat detection, reducing MTTR, and improving resilience and compliance via our Dynatrace and AWS Security Hub integration.

For more information on these integrations and the latest news:

	Announcing Amazon Bedrock AgentCore Agent Observability – Product News Dynatrace now provides native, end-to-end observability for Amazon Bedrock AgentCore agents.
	Leverage Dynatrace observability capabilities within Kiro powered by AWS – blog By integrating Kiro powered by AWS with Dynatrace, you can leverage AI-assisted monitoring and troubleshooting directly in your development workflow.
	Dynatrace Expands AWS Integrations at re:Invent 2025 – Product News Dynatrace announced expanded integrations with advanced AWS technologies and new achievements with AWS that deliver enhanced AI-driven observability, automation, and security to customers worldwide.

How Dynatrace and AWS help navigate the complexities of agentic and GenAI

Generative and agentic AI are transforming industries. However, building trust in these systems is a work in progress. In fact, according to the Dynatrace 2025 State of Observability report, 99% of AI governance leaders report their organization takes human-monitored measures to validate AI decision-making, highlighting the need for unified observability. See how Dynatrace and AWS help organizations overcome the complexities that modern AI workloads introduce.

	How Dynatrace drives value in the age of AI in the AWS® Agentic AI Marketplace – blog Agentic applications are transforming business. Discover how to operationalize AI fast with Dynatrace and the AWS Agentic AI Marketplace.
	The rise of agentic AI part 3: Amazon Bedrock Agents monitoring and how observability optimizes AI agents at scale – blog Next-level agentic AI relies on A2A communication. Discover how to optimize AI agent observability and Amazon Bedrock Agents monitoring.
	Dynatrace achieves AWS Generative AI Competency: A new milestone in observability and AI – blog Dynatrace has achieved the AWS Generative AI Competency to help organizations maximize the benefit and full potential of GenAI projects.
	Exploring the power of AI observability with Dynatrace and AWS – webinar Unpack today’s AI observability friction points and learn how AWS and Dynatrace help companies run AI with confidence.
	Unlock innovation with AI-powered observability from Dynatrace for Amazon Bedrock – fact sheet Take control of your generative AI systems with Dynatrace’s observability solutions. Gain insights, optimize performance, and build trust across your AI stack—from infrastructure to user interactions.

Discover the keys to smarter, safer innovation — faster

Constant firefighting can be a time drain for developers, site reliability engineers, security, and operations teams. Instead, they need to spend more time on business-critical tasks — most notably, innovation. That’s where Dynatrace and AWS can help, offering a strategic approach to optimizing applications for performance, cost, and security. Dynatrace and AWS provide end-to-end observability and AI-powered insights that reduce risk and accelerate modernization. From managing cloud complexity to protecting critical data and accelerating AI-driven innovation, Dynatrace on AWS provides the tools and insights teams need to succeed.

	Enhance your development workflow with the Amazon Q Developer CLI for Dynatrace MCP – blog Enhance your development workflow by integrating Amazon Q Developer CLI with the Dynatrace AI-powered observability platform using MCP.
	AWS: Driving successful cloud migration and optimization with Dynatrace – video Hear how AWS is utilizing Dynatrace to help enable customers to provide a path to make intelligent decisions and drive better business outcomes.
	Ingest and enrich AWS Security Hub findings with Dynatrace – blog Dynatrace integrates with AWS Security Hub to unify, visualize, and automate security findings across tools and environments.
	Ingest and enrich Amazon GuardDuty security findings with Dynatrace – blog This integration empowers SREs and security teams to understand runtime context for smarter threat detection, faster issue remediation, and more.
	AWS publishes Dynatrace-developed blueprint for secure Amazon Bedrock access at scale – blog Organizations can now securely and efficiently control access to Amazon Bedrock services at scale.
	Smarter cloud security with Dynatrace and Kiro CLI – blog We’ve integrated Dynatrace with AWS Security Hub and Kiro CLI to streamline triaging and remediation of critical findings, focusing efforts where they count most.

Chart your course for innovation

AWS re:Invent is more than a conference; it’s a catalyst for the next wave of technological advancement. Agentic AI is poised to redefine what’s possible, and the powerful synergy between Dynatrace and AWS provides the foundation you need to lead the way.

Visit us at booth #575 to see live demos, chat with our experts, and explore how Dynatrace can help you automate complex tasks and optimize your AWS ecosystem while accelerating adoption of generative and agentic AI technologies. Don’t miss our meetups, breakout sessions, and lightning talks to gain deeper insights and network with fellow innovators.

The post AWS re:Invent 2025: Accelerate into the age of agentic with AI-powered observability appeared first on Dynatrace news.

In today’s cloud-native environments, having real-time observability data at your fingertips is crucial. By integrating Kiro powered by AWS with Dynatrace, you can leverage powerful AI-assisted monitoring and troubleshooting capabilities directly in your development workflow.

Kiro—which recently reached general availability—helps developers by bringing structure to AI coding with spec-driven development. When a developer needs to fix an issue, investigate an error, or optimize resource usage, it’s crucial they can analyze what happened just before the issue occurred and delve deeper into the infrastructure utilization of your applications in your cloud or container environment.

Unlock development productivity with live production insights

Developers typically face restricted access to production environments, being fully dependent on site reliability engineers (SREs) or operations teams to detect and report issues post-deployment, and provide them with the necessary information to fix an issue. This segmented workflow can result in delayed problem identification and resolution, an increased risk of failures in production, and reduced efficiency throughout the development lifecycle.

By connecting Dynatrace with Kiro, developers can access real-time insights from production environments, gain contextual information down to the root cause of an incident, and receive remediation proposals—all within their Kiro environment.

Kiro has a built-in Model Context Protocol (MCP) client that can be used to extend its capabilities to communicate securely and flexibly with external data sources and tools such as Dynatrace.

Let’s dig deeper into how to leverage this capability and provide Dynatrace’s unique insights to your development teams.

Step-by-step integration guide

Prerequisites

• You’ll need a Dynatrace account. If you don’t already have one, you can start a free 15-day trial.
• Kiro must be installed on your system.
• You must have basic familiarity with AWS services and the Dynatrace platform.

Prepare integration with Dynatrace

First, you need to create a Dynatrace Platform Token, which is used to define Kiro access, and then add the required permissions for the Dynatrace MCP server.

Configure Kiro MCP Settings

The Kiro MCP configuration is managed through a JSON file. The interface supports two levels of configuration:

• User-level: ~/.kiro/settings/mcp.json applies to all workspaces
• Workspace-level: .kiro/settings/mcp.json is specific to the current workspace

You can apply the configuration using two different methods:

Method 1: Open the command palette (use Cmd + Shift + P on Mac or Ctrl + Shift + P on Windows/Linux), search for MCP and select Kiro: Open workspace MCP config (JSON) or Kiro: Open user MCP config (JSON), depending on whether or not you want to configure the settings for the workspace or user level.

Method 2: Alternatively, you can use the Kiro Panel. Open Kiro and select the Kiro ghost icon to open the left-side panel. Locate the MCP SERVERS section, select  Open MCP Config, and then start configuring the connection for the Dynatrace MCP Server.

Dynatrace specific settings

Note: Only add one of the following configurations, depending on whether you want to use the remote MCP server or the local MCP server. You can’t use both at the same time.

Using the remote MCP server

Use the following configuration. Replace $TENANT_ID with your Dynatrace environment ID. (You can find your environment ID in the URL of your Dynatrace environment — for example, https://<ENVIRONMENT_id>.apps.dynatrace.com/ui.) Then, replace $DT_PLATFORM_TOKEN with the ID of the Dynatrace platform token you created previously (for example, dt0s16.XXXXX).

{ 
"mcpServers": 
  { 
    "dynatrace": {
      "type": "http",
      "url": "https://$TENANT_ID.apps.dynatrace.com/platform-reserved/mcp-gateway/v0.1/servers/dynatrace-mcp/mcp",
      "headers": {
        "Authorization": "Bearer $DT_PLATFORM_TOKEN"
      },
      "tools": ["*"]
      }
  }
}

Connect the local MCP server

The configuration for the local Dynatrace MCP server can be added to the Kiro IDE using one-click installation or by following the manual configuration as shown below. Don’t forget to replace $TENANT_ID with the ID of your tenant.

{
  "mcpServers": {
    "dynatrace-mcp-server": {
      "command": "npx",
      "args": ["-y", "@dynatrace-oss/dynatrace-mcp-server@latest"],
      "env": {
        "DT_ENVIRONMENT": "https://$TENANT_ID.apps.dynatrace.com"
      }
    }
  }
}

Verify the integration

Once configured, you can use the Kiro chat to interact with Dynatrace through natural language conversations. Simply tell Kiro what you need, whether it’s investigating a critical incident, gaining insights into metrics, logs, or traces from your application, analyzing dependencies, or setting up automated alerts.

In the screenshot below, you can see in the lower left which capabilities are provided by the Dynatrace MCP Server. Beyond the standardized actions, such as listing active vulnerabilities or problems, querying data stored in Dynatrace, or creating a workflow, you can also interact with Davis CoPilot®, the Dynatrace natural language assistant.

Conclusion

This integration isn’t just another feature; it’s a fundamental shift in how Dynatrace integrates with your development workflow. It brings together the power of Kiro’s AI capabilities with the Dynatrace unified observability platform, allowing developers to access critical monitoring data and gain a real-time understanding of their production environments via natural language interaction.

Spend less time context switching and more time creating value for your customers. Start today and benefit from real-time insights, precise root cause analysis based on causal understanding or improved troubleshooting capabilities, and enhanced development workflows.

Explore how Dynatrace can integrate seamlessly into your development landscape using our remote MCP Server. If you’re interested in learning more about Kiro, have a look at their launch blog post or visit the documentation and dig deeper into how to connect with MCP Servers.

The post Real-time insights: Leverage Dynatrace observability capabilities within Kiro powered by AWS appeared first on Dynatrace news.

Amazon Bedrock provides enterprises with fully managed access to leading foundation models through a single API, eliminating the complexity of managing underlying AI infrastructure. This simplicity accelerates innovation but also prompts enterprises to consider how best to govern and secure access to Amazon Bedrock as they’re using it at scale.

Without a secure AI gateway in place, organizations can quickly face challenges such as:

• Uncontrolled access and data exposure: Without integrated authentication and authorization, anyone with credentials can invoke models or send sensitive data without oversight.
• Compliance and audit gaps: Without consistent tracking and isolation, it’s difficult to demonstrate adherence to internal policies or regulatory requirements.
• Operational fragility: Developers must manage credentials and request signing manually, adding complexity and security risk.

These are the same challenges Dynatrace encountered while scaling its own generative AI workloads. In response, our engineering teams developed a secure AI gateway for Amazon Bedrock, which has proven effective in serving our global user base. We’re now sharing a reusable reference architecture for the AI gateway in close collaboration with our partners at AWS.

Enterprise-grade governance for real-world use cases

The Secure AI Gateway extends Amazon Bedrock with enterprise-grade governance and control. Built on Amazon API Gateway, the solution integrates seamlessly into existing enterprise environments and provides:

• Strong authentication and authorization through integration with corporate identity systems.
• Usage quotas and throttling to manage cost and ensure fair resource distribution.
• Multi-tenant support and tenant isolation with detailed usage tracking for security, auditability, and compliance.
• Zero-code compatibility with Bedrock features: Once the AI Gateway is deployed, all existing Bedrock capabilities remain available without any integration code changes.

Proven within Dynatrace’s own platform, this reference pattern provides enterprises with a practical path to securely operationalize Bedrock, maintaining the speed and flexibility developers expect while introducing the control and transparency that enterprise governance demands.

Find all the details and the full technical walkthrough here: AWS: Building a Secure AI Gateway to Amazon Bedrock.

AI Observability for continuous insights after deployment

Securing access is only the first step; ensuring everything continues to work as intended is the next. With Dynatrace observability for Bedrock-based workloads, your teams gain continuous insight into performance, reliability, and cost, verifying that governance controls remain effective and that AI workloads perform as expected.

You can read more about our solution here: Deliver secure, safe, and trustworthy GenAI applications with Amazon Bedrock and Dynatrace.

The post AWS publishes Dynatrace-developed blueprint for secure Amazon Bedrock access at scale appeared first on Dynatrace news.

Teams can transition from setup to insights in minutes using a lightweight OTLP configuration and ready-made dashboards.

Agentic observability is evolving

Agentic AI systems are quickly moving from proof-of-concept to production, giving customers the ability to automate complex workflows, invoke a variety of different tools and APIs, and coordinate tasks across multiple services. However, traditional monitoring overlooks critical AI-specific signals, such as token consumption, model behavior, and guardrail outcomes. Teams struggle to trace non-linear agent flows, establish baselines for dynamic systems, and maintain predictable costs as usage scales. Without purpose-built observability, organizations risk degraded experiences, higher costs, and compliance gaps as agent complexity grows.

As agentic AI moves from pilot programs to production, organizations are automating complex, cross-system workflows with Amazon Bedrock AgentCore. However, most monitoring stacks weren’t designed for emergent, tool-driven behaviors and, therefore, leave blind spots around correctness, safety, and cost. Teams struggle to trace non-linear flows, establish baselines for dynamic systems, build agentic workflows, and keep token-driven spend under control as usage scales.

The observability gap in AI agent deployments

While AI agents offer significant benefits, including improved employee productivity, increased efficiency, and competitive advantage, among others, an observability gap remains, creating the following challenges:

• Complex multi-step workflows
  AI agents run non-linear, multi-system sequences with inter-agent dependencies, making data flow and responsibility hard to trace. This obscures where time is spent and who is responsible for failures in the chain.
• Limitations of traditional metrics
  Basic operational metrics often overlook AI reasoning errors and quality issues that don’t significantly affect CPU or p95 latency. Without AI-specific telemetry, subtle degradations often slip through.
• Continuous underlying agent and LLM model version changes
  Your system might be robust today, but upstream model and version updates can alter behavior, latency, and costs, forcing continuous adaptation to prevent regressions and incidents. Proactive detection of model-induced changes is crucial to maintaining stable quality and safety over time.
• Scalability and quality challenges
  As deployments grow, telemetry volume and coordination overhead surge while token usage and API calls remain untracked. This breaks cost predictability and quality control, leading to issues such as hallucinations and model drift. Multi-agent logic evolves constantly, so “normal” is a moving target. Baselines drift, complicating anomaly detection and root-cause analysis.

Without addressing these challenges, organizations face risks, from degraded user experiences and spiraling costs to compliance violations and reputational damage.

New enhancements for teams building with Amazon Bedrock AgentCore

The new Dynatrace AI Observability app embeds Amazon Bedrock AgentCore observability into a dedicated end-to-end experience, featuring out-of-the-box analytics, auto-instrumentation, targeted GenAI metrics, debugging flows, and ready-made dashboards to address all observability gaps in agent deployments. Support is available for over 20 technologies, including Amazon Bedrock, OpenAI, Gemini/Vertex, Anthropic, and LangChain.

These enhancements enable teams to take advantage of the following benefits:

• End-to-end distributed tracing
  Trace every interaction from user prompt to model reasoning to tool calls, so you can pinpoint bottlenecks, errors, or costly loops in seconds. Filter by model, provider, token usage, latency, and more to accelerate root-cause analysis.
• Enriched GenAI telemetry data, out of the box
  Each LLM and tool invocation emits spans with prompts, completions, token counts (for both prompts and completions), finish reasons, model IDs, latency, and errors, utilizing GenAI semantic attributes. Orchestration layers (for example, actions, HTTP durations, and step names) are captured for the complete workflow context.
• Cost, performance, and safety insights
  Use intelligent forecasting to detect cost and performance anomalies in token consumption and latency. Monitor guardrails for toxicity, PII, and denied topics to build trust and meet compliance requirements.
• Simple OTLP setup, fast time to value
  AgentCore already emits telemetry; simply register the OpenTelemetry export to Dynatrace once. Use your Dynatrace OTLP endpoint and token, and you’re streaming signals into the Dynatrace Grail® data lakehouse with no code rewrites. Ready-made dashboards for Amazon Bedrock let you verify ingestion and gain instant insights.

What’s next

We’re investing in a deeper Amazon Bedrock model and provider insights, expanded guardrail analytics, and additional automation so you can attach remediation playbooks to cost or safety anomalies.

Additionally, we’ll introduce a new agent visualization and topology experience that visualizes your AgentCore agents, LLM services, tool backends, and dependencies, allowing you to understand real-time topology and data flows across the entire stack.

Navigate from the topology map to traces to follow agent behavior step-by-step across services, protocols, and external calls, pinpointing hotspots, ownership, and blast radius more quickly.

Expect tighter integrations with popular orchestration frameworks and more dashboards for common agent patterns, such as retrieval, multi-agent collaboration, and tool-heavy workflows.

Get started with Dynatrace AI Observability for Amazon Bedrock AgentCore agents

Ready to learn more? Have a look at our GitHub repository.

Start instrumenting your agents today. Open the Amazon Bedrock AI Observability dashboard in Dynatrace to verify telemetry and begin your analysis.

The post Announcing Amazon Bedrock AgentCore Agent Observability appeared first on Dynatrace news.

Enterprise adoption of generative AI is showing no signs of slowing down, and it’s easy to understand why; organizations in every vertical aim to reap its benefits, including increased efficiency, routine task automation, and content generation, ultimately creating a competitive advantage. To better help organizations maximize the benefit and full potential of generative AI, Dynatrace has achieved the Amazon Web Services Generative AI (GenAI) Competency.

With this milestone, Dynatrace reinforces its position as a leading observability partner, backed by a proven track record of innovation and customer success on AWS. Building on its achievement of earning the AWS Machine Learning Competency, Dynatrace continues to drive advancements in generative AI.

Weighing the importance of this milestone for Dynatrace customers

This competency is more than just a badge; it’s a validation of how Dynatrace can help organizations safely, efficiently, and cost-effectively adopt generative AI in their business. AWS awards these competencies after rigorous technical validation and proven customer success. This means organizations can trust that Dynatrace solutions are designed to deliver measurable outcomes on AWS.

For existing customers, this competency reaffirms the Dynatrace commitment to continued innovation alongside AWS. This ensures the Dynatrace AI-powered observability platform evolves with the latest advancements in AI, future-proofing organizations’ existing investments as generative AI capabilities become core to modern cloud workloads.

For new customers, Dynatrace provides a trusted, proven foundation for observability and AI adoption on AWS. Whether an organization is exploring GenAI for customer engagement, automation, or new digital experiences, Dynatrace ensures these systems are reliable, secure, and optimized at every step.

Looking ahead with AI-powered observability on AWS

As organizations increasingly adopt generative AI, observability becomes a critical enabler. By leveraging Dynatrace causal AI, predictive insights, and seamless AWS integrations, organizations can maintain control over costs, risks, and performance while driving innovation, enhancing competitive advantage, and delivering exceptional customer experiences.

Whether you’re building, scaling, or fine-tuning GenAI application, Dynatrace and AWS Bedrock empower you to transform your observability. With end-to-end visibility into AI workloads, their interactions in full context of your business, and cloud-native applications, you can optimize performance, troubleshoot effectively, and maximize the value of your GenAI investments with greater confidence and precision.

Learn more

• Davis AI
• Dynatrace for Amazon Bedrock
• Explore the Bedrock Monitoring Playground to get hands-on with real-time dashboards that reveal how your Amazon Bedrock models behave in production.
• Install the AI Observability app to integrate Dynatrace’s AI Observability app into your environment to monitor GenAI workloads.
• Stop by booth #575 at AWS re:Invent 2025 to see Dynatrace in action.

The post Dynatrace achieves AWS Generative AI Competency: A new milestone in observability and AI appeared first on Dynatrace news.

As software systems grow increasingly complex, developers need powerful tools that can seamlessly integrate various platforms and services. Amazon Q Developer CLI has revolutionized AI-assisted development by bringing intelligent code assistance directly to your terminal. Now you can use Model Context Protocol (MCP) to bring real-time observability data directly from Dynatrace into your workflow,

Whether you’re a developer, DevOps engineer, or SRE, this integration will help you make more informed decisions faster. Through simple natural language queries, you can monitor application performance and access logs, identify issues, and automate common observability tasks across both AWS and Dynatrace platforms, without leaving the terminal or your IDE of choice.

Let’s dive in and see how you can leverage the full potential of Amazon Q Developer CLI and Dynatrace MCP server in your development process. Read on to learn how to:

1. Configure Amazon Q Developer CLI to work with MCP servers.
2. Set up the Dynatrace MCP server for real-time observability data.
3. Use natural language queries to monitor and troubleshoot applications across both AWS and Dynatrace platforms.

Breaking down the Amazon Q Developer CLI integration

Setting up this integration typically takes 15 to 30 minutes, with no additional AWS infrastructure required beyond an existing Amazon Q Developer CLI setup.

The architecture diagram below illustrates how MCP enables seamless integration between local development environments and AWS cloud services through Amazon Q Developer CLI. Through standardized MCP communication, a developer can use multiple operating systems to interact with Dynatrace observability tools and AWS offerings.

Before you begin

To complete the walkthrough, teams must first meet the following prerequisites:

• Install Amazon Q Developer CLI.
• Configure AWS CLI with the appropriate credentials (an Amazon Builder account or enterprise license is required).
• Have a Dynatrace account or get a free 15-day trial.
• Have or create a Dynatrace OAuth Client. For the needed scopes, see the Dynatrace MCP readme.

Walking through the Amazon Q Developer CLI installation process

MCP configuration in Amazon Q Developer CLI is managed through JSON files. The interface supports two levels of MCP configuration:

• Global configuration: ~/.aws/amazonq/mcp.json – Applies to all workspaces
• Workspace configuration: .amazonq/mcp.json – Specific to the current workspace

AWS MCP server setup

Once the Amazon Q Developer CLI is installed, configure it to use AWS MCP Server.

1. Open a terminal, and create the following file:
   vi ~/.aws/amazonq/mcp.json
2. Paste the following configuration into the file:

{ 
  "mcpServers": { 
    "awslabs.core-mcp-server": { 
      "command": "uvx", 
      "args": [ 
        "awslabs.core-mcp-server@latest" 
      ], 
      "env": { 
        "FASTMCP_LOG_LEVEL": "ERROR" 
      }, 
      "autoApprove": [], 
      "disabled": false 
    } 
  } 
}

Dynatrace MCP server setup

Add the configuration for the Dynatrace MCP server to mcp.json. Navigate to the Environment Variable section to set up the environment variables for Dynatrace with the needed OAuth Client Scopes. Extending our example from above, the MCP configuration file would look like this:

{ 
  "mcpServers": { 
"awslabs.core-mcp-server": { 
      "command": "uvx", 
      "args": [ 
        "awslabs.core-mcp-server@latest" 
      ], 
      "env": { 
        "FASTMCP_LOG_LEVEL": "ERROR" 
      }, 
      "autoApprove": [], 
      "disabled": false 
    }, 
    "dynatrace-mcp": { 
      "command": "npx", 
      "args": ["-y", "@dynatrace-oss/dynatrace-mcp-server@latest"], 
      "env": { 
        "OAUTH_CLIENT_ID": "", 
        "OAUTH_CLIENT_SECRET": "", 
        "DT_ENVIRONMENT": "" 
      } 
    } 
  } 
}

Verify the MCP server integration with Amazon Q Developer CLI

After configuring both AWS and Dynatrace MCP servers, verify that the integration is working properly. Run the command “q chat” in the terminal to start an interactive session with Amazon Q Developer CLI.

$ q chat 
 
To learn more about MCP safety, see https://docs.aws.amazon.com/amazonq/latest/qdeveloper-ug/command-line-mcp-security.html 
✓ dynatrace_mcp loaded in 3.48 s 
✓ 1 of 1 mcp servers initialized 
 
Welcome to  
 
 █████╗ ███╗  ███╗ █████╗ ███████╗ ██████╗ ███╗  ██╗  ██████╗  
██╔══██╗████╗ ████║██╔══██╗╚══███╔╝██╔═══██╗████╗ ██║ ██╔═══██╗ 
███████║██╔████╔██║███████║ ███╔╝ ██║  ██║██╔██╗ ██║ ██║  ██║ 
██╔══██║██║╚██╔╝██║██╔══██║ ███╔╝ ██║  ██║██║╚██╗██║ ██║▄▄ ██║ 
██║ ██║██║ ╚═╝ ██║██║ ██║███████╗╚██████╔╝██║ ╚████║ ╚██████╔╝ 
╚═╝ ╚═╝╚═╝  ╚═╝╚═╝ ╚═╝╚══════╝ ╚═════╝ ╚═╝ ╚═══╝  ╚══▀▀═╝  
 
╭───────────────────────── Did you know? ─────────────────────────╮ 
│ │ 
│ /usage shows you a visual breakdown of your current  │ 
│ context window usage │ 
│ │ 
╰─────────────────────────────────────────────────────────────────╯ 
 
/help all commands • ctrl + j new lines • ctrl + k fuzzy search 
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

The CLI is now connected to both AWS and Dynatrace MCP servers, allowing you to inquire about functionality across both AWS and Dynatrace platforms.

Testing AWS connectivity

Here are some questions you can ask q chat to verify and the expected result (note they may not be the same for everyone).

Tip: Amazon Q supports just typing “/mcp” or “/tools” and it will show you which servers are available and which tools it can call at a glance.

> Are you connected to AWS?
 
Yes, I am connected to AWS. I can help you interact with AWS services through the AWS CLI. I can make AWS API calls using the use_aws tool, which allows me to perform operations like: 
 
• Describing EC2 instances 
• Listing S3 buckets 
• Managing IAM users and roles 
• Working with other AWS services like Lambda, DynamoDB, etc.
 
Is there a specific AWS task or service you'd like me to help you with? I can run AWS CLI commands on your behalf to retrieve information or make changes to your AWS resources.
 
> Are you connected to Dynatrace?
 
Yes, I am connected to Dynatrace through the Dynatrace MCP integration. I have access to several Dynatrace-specific tools that allow me to: 
 
• Get logs for monitored entities 
• Retrieve entity details 
• List and get details about vulnerabilities 
• Execute and verify DQL (Dynatrace Query Language) statements 
• Send Slack messages via Dynatrace's Slack connector 
• Get ownership information for entities 
• View and manage problem details 
• Work with Kubernetes events 
• Create notification workflows 
 
Would you like me to perform any specific Dynatrace-related tasks for you? For example, I could get information about the connected Dynatrace environment, list current problems, or help you with DQL  
queries.

Now, we’re connected to both AWS and Dynatrace MCP servers, which means you can look at various AWS- and Dynatrace-related tasks, such as checking resources, identifying problems, getting logs, and more. Here are some sample prompts below:

Infrastructure monitoring

"Show me all EC2 instances that have high CPU usage in the last hour and correlate this with Dynatrace performance metrics" 
"List any auto-scaling events from AWS and check if they match performance degradation patterns in Dynatrace"

Application performance

"Compare the response times of our Lambda functions between AWS CloudWatch and Dynatrace monitoring" 
"Show me the slowest API Gateway endpoints and their corresponding service flows in Dynatrace"

Security analysis

"Find all security groups with open ports and cross-reference with Dynatrace security vulnerabilities" 
"Check SSL certificate expiration dates across our AWS resources and validate them against Dynatrace security monitoring"

Log analysis

"Find all error patterns in CloudWatch logs and match them with problem patterns detected by Dynatrace" 
"Show me application errors that appear in both AWS logs and Dynatrace problem detection"

Cost management

"Generate a report comparing our AWS resource costs with their performance metrics from Dynatrace" 
"Identify underutilized AWS services based on both CloudWatch and Dynatrace usage patterns"

If you want to avoid unwanted server activations and unnecessary charges during development, you can clean up with the following:

Open a terminal and navigate to the following file. Remove the MCP configurations you added as part of this setup and save them. This will ensure both AWS and Dynatrace MCP servers are not loaded when you start “q chat”.

vi ~/.aws/amazonq/mcp.json

AI-assisted development is now a reality

The integration of Dynatrace MCP with Amazon Q Developer CLI marks a significant advancement in AI-assisted development. By providing standardized access to external tools and data sources, developers can work with their production environment much more efficiently and with context-awareness. Whether you’re working on remediating problems, database optimization, code analysis, or full stack development, the combination of Amazon Q Developer CLI and Dynatrace MCP offers a powerful platform for modern development and DevOps practices.

Follow the stories: Scaling AI-assisted development with Dynatrace MCP

As the integration of AI tools like Amazon Q Developer CLI and Dynatrace MCP evolves, developers and organizations can expect even greater capabilities to streamline workflows and enhance observability. These advancements not only allow real-time insights but also empower teams to make informed decisions faster, with minimal overhead.

Interested in learning more about how Dynatrace can help you in your AI-assisted development? Check out these resources:

• Part 1 of our agentic AI series introduces MCP, A2A, and the future of automation.
• Part 2 of this agentic AI series explores how monitoring A2A and MCP communications results in better, more effective agentic AI.
• Part 3 of this series covers AI agent observability and monitoring, and how to scale and monitor Amazon Bedrock Agents.
• Full-stack observability for AI with NVIDIA Blackwell and NVIDIA NIM is covered in part 4 of this series.
• Check out Dynatrace MCP and the Dynatrace AI Observability solution for AI agent monitoring and MCP monitoring at scale.

The post Enhance your development workflow with the Amazon Q Developer CLI for Dynatrace MCP appeared first on Dynatrace news.

A generational technology shift is afoot—one where AI-powered workloads are the new currency of innovation. Agentic applications—built on foundation models, APIs, and autonomous workflows—are transforming how businesses create, deliver, and scale value.

Enterprise leaders are no longer asking if AI will play a central role in their strategy. Rather, they’re asking how fast they can operationalize AI to drive personalization, automation, and competitive advantage.

From observability to actionable intelligence

The key to unlocking AI’s full potential lies in understanding what your data is telling you. AI-native organizations don’t just need visibility; they need context, causality, and automation across their digital systems.

This is where Dynatrace excels. By combining full-stack observability with causal AI and advanced analytics, Dynatrace can help enterprises move beyond dashboards toward self-healing systems, intelligent automation, and AI-powered experiences that continuously improve.

Enhance agentic solutions with Dynatrace through the AWS Agentic AI Marketplace

To accelerate your journey in this AI-driven era, Dynatrace is proud to be part of the Amazon Web Services (AWS) Agentic AI Marketplace—Amazon’s emerging ecosystem designed to bring together agentic applications, reusable APIs, data sets, and generative AI solutions.

This integration delivers significant value, including the following:

• Seamless integration for AI workflows. Autonomous agents and builders can now readily discover and connect with Dynatrace, making it easier to compose sophisticated, intelligent workflows.
• Empower AI agents. AI agents building real-time, adaptive solutions can directly invoke Dynatrace observability, security, and automation capabilities, enabling them to make more informed decisions and take effective actions.
• Faster innovation. Enterprises benefit from faster time to value, as Dynatrace solutions are now natively composable within AWS AI services, such as Amazon Q, Amazon Bedrock, and Amazon SageMaker.

Ultimately, the AWS Agentic AI Marketplace empowers AI agents to dynamically discover, invoke, and orchestrate trusted services like Dynatrace, providing the foundation for an  autonomous and intelligent enterprise.

How Dynatrace enables business value in the agentic era

With the Dynatrace® AI-powered observability platform, organizations can benefit from the following capabilities:

• Smarter, safer automation. Dynatrace provides real-time insights into system health, anomalies, and dependencies—empowering agents to take autonomous action without compromising reliability or security.
• Accelerated AI decision-making. Dynatrace data streams feed foundational models and agentic workflows with high-fidelity, context-rich information—leading to faster, more accurate decision-making at scale.
• Personalized digital experiences. By understanding user behavior, performance trends, and business context, Dynatrace enables agents to dynamically personalize digital experiences in real time.
• Continuous optimization. From cost-aware workload placement to real-time cloud performance tuning, Dynatrace allows enterprises to scale AI workloads with confidence and efficiency.

Answering the call to innovation

The future of enterprise software is here: agentic, composable, and autonomous. For leaders navigating this massive shift, the question isn’t whether AI will play a central role, but how quickly teams can operationalize it to drive competitive advantage.

Now is the time to evaluate if your systems are truly ready—not just for AI adoption, but for AI collaboration. In this new ecosystem, it’s not simply about building AI; it’s about building with AI. This demands partners who can unlock the full potential of your data, enable your systems to adapt seamlessly, and accelerate your business outcomes. With Dynatrace, you gain a partner actively helping businesses operationalize trust, observability, and AI at scale.

The post How Dynatrace drives value in the age of AI in the AWS® Agentic AI Marketplace appeared first on Dynatrace news.

Organizations today are struggling to tame massive amounts of data by throwing myriad tools at the problem. This can result in a slower pace of innovation. For AWS customers, it can also be an opportunity to realize business benefits from existing AWS investments. The need for an AI-enabled, unified cloud observability and security platform to deliver automation and intelligence at scale across the digital enterprise has never been greater.

Partners since 2014, Dynatrace and AWS are collaborating further and deepening engagement under a new, multiyear strategic collaboration agreement. This agreement will support co-innovation and deliver unparalleled value to customers navigating their cloud modernization journeys. This partnership builds on a shared vision to empower customers with tools and insights that drive operational excellence and accelerate digital transformation.

Why this collaboration matters

For enterprises facing the challenges of rapid digital transformation, this deeper partnership between Dynatrace and AWS accelerates progress in several key areas:

• Faster innovation. Dynatrace AI and AWS’s advanced infrastructure enable businesses to adapt quickly to evolving customer demands.
• Enhanced security. Keep pace with emerging threats through continuous, automated threat detection and mitigation.
• Operational efficiency. Automation enables teams to optimize cost, performance, and resource utilization across an AWS environment.

Recognized as AWS EMEA Technology Partner of the Year at re:Invent 2024, Dynatrace has a proven track record of technical excellence, driving positive customer impact, and operational reliability. This milestone reaffirms our shared commitment to empowering customers with best-in-class solutions tailored to their needs.

Tackling key customer priorities with Dynatrace and AWS

Through this collaboration, Dynatrace seamlessly integrates its AI-powered, unified observability and security platform with AWS’s industry-leading cloud services, including Amazon Bedrock and Amazon Elastic Kubernetes Service (EKS). Together, Dynatrace and AWS are addressing key customer priorities with solutions that enable the following:

Deep, real-time observability. Gain end-to-end insights across every stage of the cloud migration and modernization journey. Dynatrace AI continuously monitors systems to prevent disruptions and identify optimization opportunities before they affect performance.

Integrated observability and threat management. Access a unified platform that combines proactive observability insights with AI-driven security threat detection to protect cloud environments at scale.

Automated performance optimization. AI and automation enhance application performance, ensuring reliability and maintaining compliance while freeing teams to focus on delivering customer value.

This strategic collaboration agreement opens new avenues for customers to harness AI-powered observability to advance their cloud innovation strategies. The Dynatrace AI Observability initiative—enhanced by features like those in Amazon Bedrock such as model customization and retrieval-augmented generation—provides clear and actionable insights into complex AI applications. This ensures cloud workloads remain secure, reliable, and optimized for peak performance, enabling teams to innovate with confidence.

By integrating with over 100 AWS offerings, Dynatrace eliminates blind spots in hybrid and multi-cloud environments, ensuring organizations can reduce operational complexity while scaling efficiently. These advancements allow teams to spend less time troubleshooting and more time delivering exceptional customer experiences.

Driving the future of cloud modernization

Whether your organization is scaling AI-powered solutions, embarking on a migration to AWS, or optimizing application performance, the enhanced partnership between Dynatrace and AWS delivers the tools, insights, and automation required to stay ahead in today’s competitive landscape.

Check out our partner story for more on how Dynatrace and AWS are redefining what’s possible in cloud transformation by placing AI-driven observability and security at the heart of innovation. To get started on your journey with Dynatrace, contact us today.

The post Dynatrace and AWS: Accelerating innovation together with multiyear strategic collaboration agreement appeared first on Dynatrace news.

Every software development team grappling with Generative AI (GenAI) and LLM-based applications knows the challenge: how to observe, monitor, and secure production-level workloads at scale. Traditional debugging approaches, logs, and occasional remote breakpoint instrumentation can’t easily keep pace with cloud-native AI deployments, where performance, compliance, and costs are all on the line. How can you gain insights that drive innovation and reliability in AI initiatives without breaking the bank?
Dynatrace helps enhance your AI strategy with practical, actionable knowledge to maximize benefits while managing costs effectively.

Amazon Bedrock, equipped with Dynatrace Davis® AI and LLM observability, gives you end-to-end insight into the Generative AI stack, from code-level visibility and performance metrics to GenAI-specific guardrails.

Developers deserve a frictionless troubleshooting experience and fast access to real-time data—no more guesswork or costly redeployments. Here’s how Dynatrace, combined with Amazon Bedrock, arms teams with instant intelligence from dev to production, helping to accelerate innovation while keeping performance, costs, and compliance in check.

Introducing Amazon Bedrock and Dynatrace Observability

Amazon Bedrock is a serverless service for building and scaling Generative AI applications easily with foundation models (FM). It provides an easy way to select, integrate, and customize foundation models with enterprise data using techniques like retrieval-augmented generation (RAG), fine-tuning, or continued pre-training.

Dynatrace is an all-in-one observability platform that automatically collects production insights, traces, logs, metrics, and real-time application data at scale.  With powerful Davis AI engine Dynatrace notifies teams about production-level issues before they disrupt users, helps predict resource usage,costs, and performance issues, and delivers guardrails that protect data and maintain compliance.

Together, Amazon Bedrock and Dynatrace provide an end-to-end observability solution for AI applications:

• Predictive operations: Proactive usage and cost forecasting to reduce unexpected operational expenses and token usage.
• Production performance monitoring: Service uptime, service health, CPU, GPU, memory, token usage, and real-time cost and performance metrics.
• Guardrail analysis: Detect hallucinations, track prompt injections, mitigate PII leakage, and ensure brand-safe outputs.
• Full-stack tracing: Track each user request across multiple FMs, vector databases, orchestrators (LangChain), and custom business logic.
• Compliance: Document all inputs and outputs, maintaining full data lineage from prompt to response to build a clear audit trail and ensure compliance with regulatory standards.

^{Figure 1. Video overview of Amazon Bedrock dashboard with Dynatrace AI and LLM Observability solution.}

How it works

Dynatrace seamlessly instruments your LLM-based workloads using Traceloop OpenLLMetry, which augments standard OpenTelemetry data with AI-specific KPIs (for example, token usage, prompt length, and model version).

Combined with Amazon Bedrock, you can:

• Spin up your AI model on Amazon Bedrock—choose from providers like AI21, Anthropic, Cohere, Stability AI, Mistral AI, Meta, or Amazon’s own Nova/Titan foundation models.
• Automatically instrument your application with OpenTelemetry.
• Configure OpenLLMetry to capture specialized LLM details as spans and metrics, like model name, completion time, token count, token cost, and prompt text.
• Send unified data to Dynatrace for analysis alongside your logs, metrics, and traces.

Behind the scenes, Dynatrace merges the standard telemetry with these advanced AI attributes, surfaces them in real-time dashboards, and applies AI-driven analytics to discover anomalies, forecast usage costs, and diagnose root causes.

How to set up and instrument your data with OpenLLMetry

Traceloop OpenLLMetry is an open source extension that standardizes LLM and Generative AI data collection. By layering on top of OpenTelemetry standards, OpenLLMetry captures the critical metrics you can’t get by default—like the number of tokens, model temperature, or guardrail triggers.

Here’s how to set it up for Amazon Bedrock:

1. Install OpenLLMetry in your Python or Node.js environment:

 pip install traceloop-sdk

from traceloop.sdk import Traceloop

headers = {

'Authorization': f"Api-Token {environ.get('DYNATRACE_TEAM_KEY')}"

}

Traceloop.init(

app_name=environ.get('DYNATRACE_APP_NAME'),

api_endpoint=environ.get('DYNATRACE_URL'),

headers=headers

)

2. Configure environment variables to send data to Dynatrace via your ingest token:

 DYNATRACE_URL =https://123abcde.live.dynatrace.com/api/v2/otlp 

DYNATRACE_TEAM_KEY=dt0.....

3. You can optionally add OpenLLMetry decorators or instrumentation to your LLM calls (for example, with LangChain or direct Bedrock SDK calls).

When your application queries Amazon Bedrock, OpenLLMetry automatically captures:

• Prompt tokens vs. completion tokens
• Finish reason (did the LLM stop due to a user request, or was the max token limit reached?)
• Model type (which Amazon foundation model or third-party model is used?)
• Performance: Response time, throughput, and error rate
  • Guardrail activations: Toxicity, PII, denied topics, and hallucinations
  • System, prompt, and completion messages and roles

This data is instantly correlated in Dynatrace so you can visualize or alert on critical thresholds (for example, if your average token usage spikes or your overall cost forecast grows beyond budget).

How to debug incorrect responses in production

Let’s walk through a real-world scenario:

Your production travel agent application—powered by Amazon Bedrock and Dynatrace—gives users incorrect travel recommendations. Perhaps it suggests flights or hotels that don’t exist or mixes up time zones. This isn’t just a minor inconvenience; it jeopardizes user experience and can directly impact revenue and trust.

Here’s how Dynatrace helps you trace and resolve the issue quickly:

Proactive alerting with Davis AI

You receive an alert from Dynatrace Davis AI anomaly detection indicating incorrect system behavior. There might be a spike in “incorrect itinerary” complaints or conversation outcomes flagged as “nonsensical.” Davis AI correlates the unusual LLM responses with application telemetry and usage patterns, so you immediately know something is off in the recommendation flow.

Full-stack end-to-end tracing

In Dynatrace Distributed Tracing, you see the entire transaction trace for the affected user session. This includes front-end requests, back-end aggregator logic, calls to Amazon Bedrock, and any vector database lookups performed for retrieval-augmented generation (RAG). Rather than sifting through multiple logs, you have a single timeline that reveals exactly where the LLM call returned unexpected data.

Inspecting the GenAI model details

By drilling down into the span data enriched by OpenLLMetry, you can see:

• Prompt and completion text and tokens used.
• The specific foundation model version (for example, anthropic.claude-v1 or amazon.nova).
• Temperature setting and max token limits.
• Any error codes or guardrail triggers.

This clarity helps you pinpoint if the model produces off-base recommendations because of a misaligned temperature, an out-of-date context, or a mismatch in user inputs.

Root cause analysis

With Dynatrace, you quickly correlate the LLM anomaly to a specific function in your microservice code. You discover that an external data source used for itinerary validation had missing or stale updates, causing the LLM prompt to reference invalid flights. You’ve found the “why” without manually spelunking logs in disparate systems.

Resolving and validating

A fix might involve updating your data pipeline or refining the prompt logic. You can deploy the change and watch in near real-time as Dynatrace collects new traces and logs. Davis AI recognizes that the anomaly is cleared, confirming that your fix resolved the incorrect responses—no guesswork required.

You can find the code example for our travel agent application here for review, and the dashboard on our Dynatrace Playground instance.

Summary

By integrating Amazon Bedrock with Dynatrace end-to-end observability, you not only catch issues early but also trace them across your entire AI stack to the root cause. Building or scaling Generative AI applications with Amazon Bedrock requires robust insights into your environment—from model usage and performance metrics to cost forecasts and guardrail efficacy.

Dynatrace helps you scale with:

• Complete end-to-end tracing across your services, external data pipelines, and LLM calls.
• Predictive analytics that forecast AI resource usage and cost trends, letting you proactively manage budgets.
• Unified dashboards that bring performance, cost, code-level data, logs, metrics, and audit events together.
• Compliance and governance that integrate security checks, data masking, and guardrail analysis.

Whether you’re a developer racing to put your latest AI-powered application or a new feature into production or an SRE ensuring your system meets enterprise-grade SLAs, Dynatrace and Amazon Bedrock help you to create frictionless AI applications, focusing on performance and observability—at any scale, in production, with no downtime.

Useful resources

• Dynatrace Amazon Bedrock instrumentation documentation
• Supported GenAI technologies
• Dynatrace Playground demo
• Video webinar: AI and LLM Observability with Dynatrace

The post Deliver secure, safe, and trustworthy GenAI applications with Amazon Bedrock and Dynatrace appeared first on Dynatrace news.

Dynatrace integrates with Amazon EventBridge to break the silos between DevSecOps teams by unifying security findings along the Software Development Lifecycle (SDLC) and enriching them with runtime context. Powered by OpenPipeline™, Dynatrace allows you to ingest, visualize, prioritize, and automate security findings, helping to reduce noise from alerts and provide focused remediation to the issues that matter to your critical production environments.

The complexity of multicloud environments

In complex multicloud environments, security findings are often siloed across build-time and runtime tooling, as well as spread across various environments. Thus, getting a holistic view of your security posture and risks is challenging. The consequences include:

• Time spent navigating various platforms to collect data.
• Difficulty prioritizing findings from disparate tools.
• Security coverage gaps.
• Excessive manual effort is required to notify stakeholders of critical findings.
• Remediation takes a long time.

Moreover, with the number of security findings generated, your DevSecOps teams might become overwhelmed and miss important issues that directly impact your production services and applications. A good example is a critical severity vulnerability discovered in a build-time artifact, such as a container image that isn’t deployed and doesn’t impact your runtime. Your DevSecOps teams shouldn’t be distracted by such findings and should instead focus on vulnerabilities in your production application that are exposed to the internet and present a real risk.

The Dynatrace solution

Dynatrace addresses these issues by providing unified security events ingest and analysis of security findings across cloud environments. The ingested findings are mapped to the monitored runtime entities, which allows you to assess the risks better and reprioritize remediation of the critical findings.

Security findings can be pushed to Dynatrace, as with Amazon EventBridge, or pulled from a third-party tool by a dedicated Dynatrace integration.

With built-in support for various products and security-finding standards, Dynatrace provides visibility into security posture from multiple stages of your SDLC. This allows you to orchestrate the findings effectively, drive faster remediation, discover security coverage gaps, optimize tooling usage, and maximize your ROI.

Ingest AWS EventBridge findings into Dynatrace

Dynatrace partners with AWS and serves as a destination for Amazon EventBridge rules. Depending on the use case, findings and logs can be forwarded to the dedicated OpenPipeline endpoints and ingested into Grail^TM.

Dynatrace supports security findings forwarded via Amazon EventBridge in the following scenarios:

• Ingested as raw events or in a supported generic standard data format, such as OCSF or ASFF.
• Forwarded as findings from the AWS Security Hub, including vulnerability, detection, and compliance events.
• Forwarded as container findings from the Amazon ECR (basic and enhanced scanning).

Dynatrace maps the ingested events to Semantic Dictionary conventions for the supported products and data formats. You can consume the events uniformly for visualization and analysis in Dashboards and Notebooks and automation use cases in Workflows.

For example, you can ingest Amazon ECR container image vulnerability findings into Dynatrace using Amazon EventBridge. Dynatrace provides a CloudFormation template and detailed instructions as part of the setup.Please read our documentation for individual integrations, Ingest Amazon ECR vulnerability findings and scan events, Ingest AWS Security Hub security findings, and our blog post, Enrich AWS ECR vulnerability findings with runtime context, for additional details on the integration setup and supported use cases.

Get started

Also, check out Amazon ECR monitoring & observability

Try it today

Leverage a seamless, out-of-the-box experience to optimize performance, reduce costs, and drive cloud-native innovation.

Contact your Dynatrace representative or visit our AWS integration page to start your free trial and see the difference intelligent cloud monitoring can make.

The post Ingest and enrich security findings delivered by Amazon EventBridge with Dynatrace appeared first on Dynatrace news.

Dynatrace is proud to be the first AWS global tech partner to offer ready-to-use CloudFormation templates for validating the Well-Architected pillars. The Dynatrace CloudFormation templates are now available in the AWS CloudFormation Public Extensions Registry, which makes Well-Architected validation even more accessible for AWS users.

Dynatrace and the AWS Well-Architected Framework ensure continuous optimization of cloud architecture, aligning with the key pillars of operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. Our previous blog post discussed the importance of integrating Dynatrace with the AWS Well-Architected Framework to enhance your software development lifecycle (SDLC). By leveraging Dynatrace Workflow Automation and the Site Reliability Guardian, organizations can ensure that their applications meet AWS’s best practices across all pillars.

The Dynatrace CloudFormation templates provide a powerful, easy-to-deploy approach that helps AWS users automate and integrate Dynatrace into their environments with minimal effort. It accelerates the process of validating and optimizing cloud infrastructure.

A quick start for AWS users

AWS users can now benefit from ready-to-use CloudFormation templates that allow for the rapid deployment of Dynatrace workflows designed to validate your cloud architecture against the AWS Well-Architected Framework. These templates are available directly from the AWS CloudFormation Public Extensions Registry, enabling any AWS user to integrate Dynatrace’s observability and release validation capabilities into their infrastructure in just a few steps.

This quick-start approach is especially beneficial for organizations looking to streamline their SDLC and ensure their applications align with the AWS Well-Architected Framework before production deployment. The templates also serve as a foundation for extending workflows, allowing you to tailor the solution to specific business needs and requirements.

Key features of the Dynatrace CloudFormation templates

• Out-of-the-box validation for AWS best practices: These CloudFormation templates automatically provision the necessary Dynatrace resources to continuously validate your infrastructure against AWS Well-Architected Framework pillars. This means you can automatically review your applications for performance efficiency, security, cost optimization, and more.
• Seamless deployment: Once you download them from the AWS Public Extensions Registry, you can deploy these CloudFormation templates with minimal effort. They help teams automate best practice checks and validations in minutes, reducing manual intervention and ensuring faster, more reliable deployments.
• Automation at scale: Using Dynatrace Workflow Automation available through these templates, you can automate the release validation process across your entire AWS environment. This includes vital capabilities such as continuous release validation, automated rollback mechanisms for failed validations, and integration with your CI/CD pipelines for full automation throughout the SDLC.
• Extensible for custom needs: While the templates provide a robust foundation, they’re also extensible. Whether you need to integrate additional validation criteria or customize workflows for specific services, you can easily modify the templates to fit your unique requirements.

Dynatrace CloudFormation templates accelerate the path to production

Implementing the Well-Architected Framework for organizations using AWS is critical to building secure, reliable, and cost-efficient cloud applications. However, this can often be a time-consuming process involving manual validations and checks at multiple stages of the SDLC.

By using the new Dynatrace CloudFormation templates, AWS users can automate the validation of their cloud infrastructure, vetting every release against the core pillars of the AWS Well-Architected Framework before it reaches production. This reduces the risk of performance or security issues and helps organizations adopt cloud-native practices faster by automating tedious manual tasks.

Moreover, because the Site Reliability Guardian is included in these templates, you can continuously monitor and validate essential application metrics like response times, CPU usage, and error rates to verify your application meets its service level objectives (SLOs) before every deployment. This drastically reduces the likelihood of post-release issues and provides peace of mind for development teams.

How to get started with the Dynatrace CloudFormation templates

Getting started with these new CloudFormation templates is easy:

• Access the templates: Head to the AWS CloudFormation Public Extensions Registry and search for the third-party extensions of Dynatrace.
• Deploy the solution: Use the CloudFormation console to deploy the templates in your Dynatrace environment.
• Extend as needed: Once deployed, you can customize the Dynatrace workflow to fit your specific SDLC requirements, adding additional validation criteria or integrating with other AWS services like Lambda, S3, or EC2.
• Monitor and optimize: With Dynatrace now fully integrated, you can continuously monitor application performance, detect anomalies in real- time, and optimize your infrastructure on AWS based on actionable insights.

Streamlining the SDLC

Adding Dynatrace CloudFormation templates in the AWS Public Extensions Registry is a game-changer for organizations looking to streamline their SDLC according to the AWS Well-Architected Framework pillars. With ready-to-use automation workflows, seamless integration into AWS, and the ability to extend and customize the solution, these templates provide a fast, powerful way to improve your cloud applications’ reliability, security, and efficiency.

By leveraging this new capability, AWS users can now automate what was once a manual, error-prone process, confidently accelerating their path to production.

Check out the AWS CloudFormation Public Extensions Registry to get started today and bring Dynatrace-powered validation into your AWS SDLC.

The post Automating AWS Well-Architected Framework validations with Dynatrace CloudFormation templates appeared first on Dynatrace news.

The rapid evolution of cloud technology continues to shape how businesses operate and compete. At AWS re:Invent 2024, Dynatrace showcased a suite of new AWS and Dynatrace integrations designed to enhance cloud performance, security, and automation. These innovations promise to streamline operations, boost efficiency, and offer deeper insights for enterprises using AWS services. Together, Dynatrace and AWS are paving the way for more robust and agile cloud solutions. This blog post will explore these exciting developments and what they mean for organizations.

Tried and true: AWS and Dynatrace

With over 100 out-of-the-box AWS integrations, Dynatrace and AWS have strengthened their collaboration further by bringing forth innovations that address the dynamic needs of modern enterprises. With a shared focus on enhancing cloud capabilities, this partnership underscores the significance of observability and automation in cloud environments. AWS’ recent recognition of Dynatrace as the 2024 AWS EMEA Technology Partner of the Year highlights the joint commitment to accelerate customer cloud transformation.

Dynatrace enhances support for Amazon EKS Hybrid Nodes

We are proud to announce our support for the launch of Amazon EKS Hybrid Nodes, which enable organizations to run Kubernetes workloads seamlessly across hybrid environments. With deep observability and AI-driven insights delivered by Dynatrace, customers can achieve optimal performance and reliability of their applications, whether running on-premises or in the cloud.

This integration empowers organizations to optimize their Kubernetes workloads by providing continuous observability across the entire stack, ensuring that hybrid cloud deployments are not only efficient but also resilient. Dynatrace automatic root-cause analysis and intelligent automation enables organizations to accelerate their cloud journey, reduce complexity, and achieve greater agility in their digital transformation efforts.

Streamlining observability with Dynatrace OneAgent on AWS Image Builder

In our ongoing collaboration with AWS, we’re excited to make the Dynatrace OneAgent available as a first-class integration on AWS Image Builder via the AWS Marketplace. This integration simplifies the process of embedding Dynatrace full-stack observability directly into custom Amazon Machine Images (AMIs). This integration augments our existing support for OpenTelemetry to provide customers with more flexibility.

By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics. This seamless integration accelerates cloud adoption, allowing enterprises to maximize the value of their AWS infrastructure and focus on innovation rather than managing observability configurations.

VMware migration support for seamless transitions

For enterprises transitioning VMware-based workloads to the cloud, the process can be complex and resource-intensive. New VMware migration support from AWS simplifies this transition, reducing the challenges associated with moving critical workloads. With real-time analytics and insights, Dynatrace plays a key role in supporting organizations by providing enhanced performance, operational efficiency, and the insights needed to understand consumption and avoid over provisioning throughout the migration. This is particularly valuable for enterprises deeply invested in VMware infrastructure, as it enables them to fully harness the advantages of cloud computing.

Additionally, Dynatrace enhances the migration process by providing advanced monitoring and actionable insights, empowering businesses to minimize downtime and improve application performance. Dynatrace integrations with AWS services like AWS Application Migration Service and Migration Hub Strategy Recommendations enable a more resilient and secure approach to VMware migrations to the AWS cloud.

Automating AWS Well-Architected Framework compliance with Dynatrace and AWS CloudFormation

In collaboration with AWS, Dynatrace is introducing a new CloudFormation template that automates the validation of the AWS Well-Architected Framework, incorporating Dynatrace observability and Site Reliability Guardian to enforce quality gates within CI/CD pipelines. This solution aligns to the AWS Well-Architected Framework.

By embedding Dynatrace AI-driven observability and reliability checks into the deployment pipeline, organizations can proactively assess their cloud architectures against best practices, detecting and resolving potential issues before they impact production. This integration showcases the strength of our partnership with AWS, helping joint customers achieve cloud governance, enhance scalability, and optimize their digital applications for maximum efficiency and resilience.

Gaining precise insights with Dynatrace integration for AWS EventBridge

Now supporting a deeper integration with AWS EventBridge, Dynatrace is able to act as a consumer of AWS events. By ingesting EventBridge events into the Dynatrace platform, customers can leverage AI-powered contextual insights to gain a deeper understanding of their cloud environments.

This integration allows organizations to correlate AWS events with Dynatrace automatic dependency mapping, real-time performance monitoring, and root-cause analysis. As a result, businesses can improve incident response, optimize application performance, and streamline operations with actionable insights. This collaboration highlights the strength of the Dynatrace-AWS partnership in providing unmatched observability, enhancing service reliability, and enabling customers to innovate with confidence at cloud scale.

AWS and Dynatrace deliver transformative cloud integrations for joint customers

AWS re:Invent 2024 once again demonstrates the power of innovation in cloud technology. The new Dynatrace and AWS integrations announced at this event deliver organizations enhanced performance, security, and automation.

At Dynatrace, we are continually pushing the boundaries of what’s possible in cloud observability by partnering with AWS to deliver solutions that help organizations achieve excellence in their cloud journeys. Whether it’s simplifying Kubernetes management with EKS Hybrid Nodes, streamlining observability with simplified instrumentation on AWS Image Builder, automating compliance with the Well-Architected Framework, or gaining deeper insights with EventBridge, our integrations with AWS are designed to empower our customers to succeed.

As digital transformation continues to accelerate, Dynatrace remains committed to delivering the tools, automation, and insights organizations need to drive innovation, improve operational efficiency, and achieve superior business outcomes on AWS.

Stay tuned for more exciting updates as we continue to expand our collaboration with AWS and help our customers unlock new possibilities in the cloud.

^{Dynatrace, OneAgent, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. Third-party trademarks mentioned in this blog are the property of their respective owners.}

The post New integrations announced at AWS re:Invent enhance cloud performance, security, and automation appeared first on Dynatrace news.

For successful cloud modernization, organizations require a holistic view of cloud environments and intelligent automation to ensure seamless migrations. Key themes at AWS re:Invent 2024 will include cloud innovation and AI transformation.

As more organizations move their operations to the cloud, it is crucial for teams to have visibility and control over their applications and infrastructure to ensure smooth and efficient operations. Incorporating AI and automation into your observability strategy is essential for a successful migration journey and optimized operations across your full IT environment.

Dynatrace partners with AWS to provide advanced observability for cloud environments, empowering organizations with real-time insights into their applications and infrastructure. By leveraging a power-of-three approach to AI and an efficient data lakehouse architecture, Dynatrace discovers and pinpoints problems automatically, enabling organizations to automate confidently and deliver better, more reliable services with AWS.

This year at AWS re:Invent, Dynatrace will be showcasing its latest innovations and thought leadership on how organizations can leverage AI and observability to get the most out of AWS services.

An innovative partnership: Dynatrace and AWS

This year’s AWS re:Invent promises to be brimming with innovation and collaboration; and Dynatrace is thrilled to be at the forefront of advancing cloud observability. We are excited to announce new integrations with AWS services, designed to enhance AI-driven automation and analytics capabilities for our joint customers.

Our partnership with AWS continues to build, enabling seamless data flows and real-time processing that help organizations harness the full potential of their cloud environments. Attendees can look forward to demonstrations of how these integrations streamline operations, boost performance, and drive efficiencies across diverse AWS workloads.

The need for intelligent observability in AWS clouds

AWS cloud environments are dynamic and complex. They require constant monitoring to ensure everything runs smoothly. Intelligent observability provides real-time insights into cloud operations, identifying issues before they impact performance. With AWS’s vast array of services, having a comprehensive observability strategy is crucial.

As organizations migrate more workloads to the cloud, the need for seamless integration and automation increases. Intelligent observability helps manage these transitions, reducing downtime and minimizing risks. By leveraging observability, IT managers can focus on strategic initiatives rather than firefighting operational issues.

Intelligent observability is not just about monitoring; it’s about gaining actionable insights. By understanding data trends and anomalies, organizations can make informed decisions, optimizing their AWS cloud infrastructure for better efficiency and cost savings. Check out the resource below to learn more.

Why modern, well-architected AWS clouds demand AI-powered observability – whitepaper Cloud observability is essential at every stage of the modernization journey, providing the insights and intelligence necessary to accelerate and secure the adoption of AWS technologies.

At the front of innovation: First AWS partner to natively integrate with AWS App Migration Service

Dynatrace has become the first AWS partner to achieve native integration with the AWS Application Migration Service. This seamless integration allows organizations to leverage Dynatrace AI-driven observability for more efficient application migrations. With automatic detection of performance issues, security concerns, and other potential problems, this collaboration simplifies and accelerates the migration process while enhancing security.

Check out the resource below to learn how the integration of AWS Application Migration Service with Dynatrace benefits organizations by making their migrations faster, easier, and more secure.

Dynatrace becomes the first AWS partner to integrate with AWS Application Migration Service – blog AWS Application Migration Service allows customers to deploy Dynatrace OneAgent to easily spot performance impacts when migrating to the cloud.

Meet us at AWS re:Invent 2024

AWS re:Invent 2024 promises to be an exciting event filled with groundbreaking insights and innovations. Dynatrace invites you to visit our booth #614, where our experts will be available to discuss your cloud modernization needs. We are committed to helping you leverage the full potential of AWS, ensuring your cloud infrastructure is both robust and agile.

By attending our sessions, you’ll gain valuable insights into how the Dynatrace intelligent observability platform can transform your AWS cloud experience. Learn from real-world case studies and hear directly from our customers about the tangible outcomes they’ve achieved.

We look forward to sharing our vision for the future of cloud modernization. Together, we can accelerate your AWS cloud transformation, ensuring your organization remains competitive in today’s digital landscape.

The post AWS re:Invent 2024 guide: Cloud observability and AI transformation appeared first on Dynatrace news.

Dynatrace integrates with AWS Elastic Container Registry (ECR) to enable visibility, orchestration, and prioritization of cross-container-registry vulnerability findings. This integration provides a single pane of glass for container image scans of your containerized applications and is part of a larger effort to enrich vulnerability findings with runtime context.

In complex multicloud environments, vulnerability findings are often siloed between build-time and run-time tooling. Thus, getting a holistic view of security risks is challenging.

Dynatrace addresses this issue by providing unified ingest and analysis of container vulnerability findings across cloud and container registries. This ensures that SecDevOps has a continuous and comprehensive understanding of its security posture.

In addition, security findings detected during the build phase and in your artifact registries, such as Amazon ECR, might not be relevant to your production-critical applications. By enriching runtime context from the monitored entities, Dynatrace helps filter out the noise, prioritize critical findings, and focus your remediation efforts on what truly matters for your production environment.

Key Steps in the Integration Process

Container image scanning

Amazon ECR scans container images for vulnerabilities. You can choose between basic and enhanced scanning.

Data ingestion

The vulnerability findings are pushed into the Dynatrace platform through AWS Event Bridge via the dedicated security ingest endpoint powered by OpenPipeline^TM. You can set it up using an AWS CloudFormation template provided by Dynatrace. For instructions, see the documentation.

Data mapping

The ingested data is mapped according to the Dynatrace Semantic Dictionary, ensuring a unified format for analysis.

Analysis and automation

Once the findings are ingested, you can visualize, analyze, and automate in Dynatrace with Dashboards, Notebooks, and Workflows.

Use cases

Once security findings and scan events are ingested into Dynatrace Grail™, you can analyze them and perform automation tasks, leveraging the uniform data format.

Amazon ECR ingested data can be consumed as follows:

• Dashboards: Use the provided sample dashboards or create custom visualizations of the security findings and scan events.
• Notebooks and Security Investigator: Use vulnerability findings as an additional dimension for threat hunting and forensic investigations.
• Workflows: Automate the orchestration of critical vulnerability findings by creating alerts and tickets.

Explore individual use cases in Dynatrace Documentation:

• Visualize and contextualize security findings
• Automate and orchestrate security findings
• Discover coverage gaps in security scans
• Visit the Dynatrace Hub and leverage our Amazon ECR app

Get started

Visit Dynatrace Documentation and get started setting up your Amazon ECR data integration.

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