From generic AI to task‑focused operational agents

Dynatrace ready‑made agents are purpose‑built capabilities that apply Dynatrace intelligence to specific, recurring operational tasks. Each agent focuses on a clearly defined problem, such as explaining why a service is slow, summarizing unusual behavior in an environment, or helping you understand what changed and why it matters. These agents are designed to take a question or a signal based on the exact data that is in your environment and organization and turn it into a useful answer you can act on.

Because Dynatrace agents are ready‑made, there is no need to define prompts, train models, or design behavior from scratch. Each agent already knows:

• What type of input to expect,
• Which Dynatrace signals and context it should use,
• And what output types are most useful for each addressed problem type.

All available ready-made Dynatrace agents can be found in Dynatrace Hub.

Trigger agent actions with Dynatrace Workflows and the Dynatrace MCP Server

Ready‑made agents can be triggered automatically as part of Dynatrace Workflows or available wherever you already work via the Dynatrace MCP Server.

Using agents in Dynatrace Workflows

Dynatrace Workflows lets you run agents in response to events or on a schedule. Instead of manually asking questions about potential problems and remediation steps, the workflow autonomously responds to changes in your environment.

For example, the Kubernetes Troubleshooting Agent runs nine parallel queries for data enrichment, and Dynatrace Intelligence turns all the information into a structured diagnosis. Customize the agents to your needs, including instructions for human approval steps and automated remediation.

The fastest way to get started is with Dynatrace ready-made agentic workflow templates, currently available in a preview release. Instead of building from scratch, you get proven automations that summarize issues, suggest remediation, and deliver insights directly to the tools your teams already use.

Power users can go further by building their own agentic workflows that combine Dynatrace Intelligence actions with any trigger, data source, or integration in Workflows. Use cases range from auto-scaling Kubernetes clusters based on Dynatrace Intelligence forecasts to generating query-cost-optimization recommendations for stakeholders, to virtually any other automation your environment requires.

Using agents through the Dynatrace MCP Server

The Dynatrace MCP Server makes the agents available outside the Dynatrace web UI, without requiring you to deploy or operate any additional infrastructure. You can connect Dynatrace to any MCP‑compatible client in minutes, with no server to install, host, or maintain.

Through the tools exposed by the MCP Server, you can use natural language to query data in Grail®, check system health, and get problem analyses and remediation recommendations. This brings Dynatrace directly into the tools you already use, such as your IDE, Claude Code and Cowork, Microsoft Copilot, Slack, or automation platforms like n8n. The Dynatrace MCP Server also powers integrations with systems like Azure SRE, AWS DevOps, GitHub Copilot, Atlassian Rovo Ops, Amazon Q, and others.

This means agents are no longer tied to a single interface. You can ask Dynatrace questions and get grounded, production‑ready answers wherever you work, using the same agents and intelligence that power Assist and workflows.

Dynatrace Assist: a simple way to test ready-made agents

The quickest way to use a ready‑made agent and see how it works before you start creating a workflow is with . Dynatrace Assist lets you ask questions about your environment using natural language, without switching tools or setting anything up.

A simple way to start is with a real problem you already have. For example, when a service becomes slow, open Assist and ask a question such as “Summarize the open problems and highlight those that need immediate attention.” Assist interprets the question, evaluates the environment you’re working in, and pulls together relevant data and context using Dynatrace Intelligence. Instead of manually navigating metrics, traces, logs, and dependencies, you get an explanation grounded in what is actually happening in your system.

Continuing your conversation with Assist, you can refine the question or follow suggested drill‑downs. Assist supports this as a single flow, helping you move from an initial question to deeper analysis and, where applicable, to next steps. You’re not configuring an agent or defining behavior. You’re simply asking a question and letting Dynatrace coordinate the right intelligence and ready‑made agents behind the scenes.

This makes Assist your lowest‑friction entry point for using Dynatrace agents. You get a concrete result quickly, using the same data and context you already rely on in your daily work.

What’s next?

If you haven’t already, open Dynatrace Playground, or your Dynatrace tenant, and ask Dynatrace Assist a question to see the ready-made agents in action.

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What Agent Mode can do with Dynatrace observability data

Once connected with Dynatrace MCP Server, Postman’s Agent Mode works with real runtime data rather than just API requests and responses. When an API test fails or behaves unexpectedly, the agent can correlate the behavior with Dynatrace telemetry such as service metrics, traces, logs, and detected anomalies.

This allows developers to quickly understand whether an issue is caused by the API itself or by what’s happening behind it, such as a slow dependency, a failing backend service, or a recent change impacting runtime behavior. Because this context comes directly from Dynatrace, developers can ask follow‑up questions in natural language and get answers grounded in production data. All of this happens inside the Postman workflow, without switching tools or manually querying observability dashboards.

The result is a faster feedback loop: APIs can be tested, validated, and debugged with awareness of how they actually behave in live environments, not just how they respond in isolation.

Explore Dynatrace use cases with Postman

This integration is most valuable in situations where API behavior needs to be understood in the context of how systems actually run, not just how endpoints respond in isolation.

Common scenarios

Instantly check Dynatrace environment health in Postman

Quickly connect Dynatrace as an MCP server in Postman, set up environment variables, and use natural language prompts to get a real-time overview of open problems in your monitored environment. This streamlines troubleshooting by surfacing actionable issues directly in Postman, saving you time and reducing context switching.

Diagnose and resolve API failures with Dynatrace insights

By running API tests and intentionally triggering failures, you can leverage the MCP server to pinpoint the root causes of errors, such as backend issues or logic bugs, using the real-time insights from Dynatrace. This empowers developers and testers to quickly distinguish between code and infrastructure problems, accelerating debugging and improving application reliability.

Get a holistic application health assessment in Postman

You can ask the MCP server in Postman broad, natural-language questions about the overall health of your application and the services monitored by Dynatrace. The server provides a summary of performance and health across multiple services, highlights slow endpoints, and offers actionable recommendations. This allows you to quickly identify bottlenecks and focus on critical issues, supporting proactive performance management and efficient troubleshooting.

Automate continuous health checks with Postman collections

You can have Postman automatically generate collections that call Dynatrace endpoints on a schedule, enabling ongoing performance and health monitoring between releases. This ensures teams are proactively alerted to issues, supporting continuous delivery and higher service quality.

Reduce guesswork in AI-assisted workflows

AI‑assisted development is only as effective as the context an agent can access. Without reliable runtime data, agents are limited to reasoning from API definitions, test results, and assumptions, making it difficult to distinguish real system issues from surface‑level symptoms.

By connecting Postman’s Agent Mode to Dynatrace through MCP, agents can ground their reasoning in trusted observability data from live systems. This gives the agent access to the same production signals developers rely on today, such as service health, performance trends, errors, and dependencies, rather than relying on inferred behavior alone.

The result is more actionable AI assistance. Instead of guessing why an API behaves a certain way, the agent can explain what is happening in the system and why, reducing false conclusions and improving the quality of recommendations in AI‑driven workflows.

Try Postman’s Agent Mode with Dynatrace

The Dynatrace MCP Server is available in the Postman MCP Catalog and can be connected to Postman’s Agent Mode to bring runtime observability data directly into API workflows.

• Learn more about the Dynatrace MCP Server on Dynatrace Hub

• Try out Agent Mode with the Dynatrace MCP server:

For teams already using Dynatrace and Postman, this is a straightforward way to add production context to AI‑assisted API development. For teams exploring Agent Mode, it provides a practical foundation for grounding agent workflows in real system behavior.

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The Dynatrace platform provides expanded data access via the Dynatrace MCP server and several APIs that tie into agentic integrations from our ecosystem, our IDE plugins, CLIs, and Dynatrace® Apps. The Dynatrace MCP server has been a cornerstone of making this innovation available to our developer audience right in the IDE. However, MCP is heavily relying on text-based output. This blog introduces new Dynatrace MCP Server application support, which we’re happy to contribute to the community, upgrading UI capabilities by enabling data visualization. It empowers developers and organizations to build their own agentic platforms and visualize experience data, without relying solely on LLM reasoning.

Why are MCP applications so valuable to Developers?

A picture is worth a thousand words

Charts, color cues, and visual tables are here for a reason. We just need a quick look to determine whether something is right or wrong, and with MCP Apps, those visuals can appear in the same conversation with Dynatrace Assist or when interacting with Dynatrace Intelligence from within Kiro, GitHub Copilot, or any other MCP integration, where we ask questions while avoiding context switching and speeding up analysis and result checking. Once we get the insights we need, we can dive even further into the platform applications with comprehensive context.

For example, during a problem investigation, we observe a spike in requests across all endpoints, especially for the v1/trade/long/process endpoint.

Dynamic visual results

Interactive UIs let users modify results per their needs. With the new visual results, we can filter, pivot, and drill into results without re-issuing prompts; the model and UI remain in sync and continue the conversation with a richer context.

Actionability built in

Visuals can include action buttons, for example, Open in Notebooks, which calls back to the server to drill into the data stored in Dynatrace Grail® unified data lakehouse. You can create dashboards and notebooks directly from any page without changing context.

Human verification

Human involvement in data analysis is one of the factors that differentiate these processes from purely LLM-based solutions. Keeping humans in the loop to validate and interpret results visually is one of the most important factors for organizations that want to maintain control over their data analysis process.

MCP App architecture

MCP extension protocol integration

The Dynatrace MCP Server uses the @modelcontextprotocol/ext-apps library to register interactive UI applications alongside traditional text-based tool responses. This allows the MCP client (such as GitHub Copilot) to extend the standard MCP results with visual capabilities.

Tool-to-UI binding

Tools are registered via the _meta.ui.resourceUri property that links the tool’s output to its visual counterpart. When execute_dql returns results, the host knows to render the associated UI app as well.

Data flow

Structured output parsing

The tool returns results as structured markdown containing metadata and a JSON block. The UI app parses this response, extracting record counts, warnings, and the actual data records.

Real-time client-side rendering

A lightweight TypeScript-based web-app (execute-dql.ts) receives the tool result via the app.ontoolresult callback and dynamically builds an interactive HTML table—complete with sortable columns, hover states, and expandable JSON cells.

Single-file bundling

When using Vite with vite-plugin-singlefile, the HTML, CSS, and TypeScript are bundled into a self-contained HTML resource that the MCP server serves on demand.

See it in action

Take a look at this example video showing an investigation into slowdowns.

In the following screenshots, you can see examples of how data that is structured in a visual way helps us to ask the right questions or continue investigations promptly.

In reviewing error logs, we see frequent errors appearing in easytrade-broker-service.

While analyzing service degradation due to the slowdown, we found that two services were affected for approximately 30 minutes.

What about security?

Let’s call out the elephant in the room: security is a major concern with MCP servers, and adding HTML rendering on top is likely to raise eyebrows.

There are a couple of security measures and best practices in place to protect our users:

1. Dynatrace MCP Apps are always rendered in a sandboxed iframe controlled by the host.
2. Our MCP apps communicate only with the MCP host via JSON RPC. No direct communication with the internet occurs with Dynatrace MCP apps.
3. We make use of battle-tested React components, inheriting all security-relevant features of React.

Get started

Dynatrace MCP Apps is already included in the latest version of our local MCP Server, and will soon be added to our remote server as well. If you’re already using the local MCP server, you only need the updated version, which should be automatically available in your Dynatrace environment. If the update doesn’t happen automatically, or if you aren’t already using the local Dynatrace MCP server, install it via the GitHub MCP registry and start experimenting using the provided quickstart.

To better understand how you can uplevel your AI assistants with live production insights from Dynatrace, read our most recent blog post or explore the Dynatrace Agentic AI ecosystem in Dynatrace Hub.

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AI is changing the way we work, from boosting our efficiency and executing tasks on our behalf. As AI enters the agentic era, Dynatrace Assist is your trusted partner for working smarter with Dynatrace. Powered by Dynatrace Intelligence, it helps you solve problems faster and brings AI into your daily workflow.

Today, we’re announcing Dynatrace Assist, our next-generation AI chat that goes far beyond answering questions. It lives where you work, understands your environment, and helps you get real work done with the full power of Dynatrace Intelligence behind every interaction.

Dynatrace Assist is the evolution of Davis CoPilot®, but make no mistake: this is much more than a rebranding. It’s a paradigm shift in capability, outcome, and value.

Your agentic partner helps you gather insights

Dynatrace Assist is where the full power of Dynatrace Intelligence, the new Dynatrace agentic operations system, is available at your fingertips. Instead of moving between dashboards, queries, and tools, you can simply start with the conversational interface: ask a question and let Dynatrace Intelligence do the work.

Dynatrace Intelligence interprets your prompt using generative, causal, and predictive AI capabilities to understand what you’re trying to achieve. Like a member of your team, Assist pulls together context from Grail, maps relationships utilizing Smartscape’s dependency graph, and collaborates in real time with Dynatrace agents. As the conversation unfolds, you move from understanding to exploration—following suggested drill-downs, refining your analysis, and understanding the next steps. This allows for a single, seamless flow from question to insight to execution.

Multi-step reasoning that understands your environment

Assist doesn’t just do what you ask—it thinks several steps ahead. It helps you understand the why, decide what to do, and see what comes next. And all of this is fully transparent, showing you exactly which data backs each decision, so you can trust the recommendations and proceed decisively and confidently.

Under the hood, Assist combines Grail’s unified data lakehouse with Smartscape’s dependency graph, layered with our causal and predictive AI. By leveraging the same tools that are hosted on the Dynatrace MCP Server, Assist can pull insights from any part of your environment and kick off deeper analysis when needed.

From incident to remediation in a single flow

AI on its own is meaningless. AI needs context and data to deliver real value, and you need intelligence that understands your environment and helps you move faster with confidence. Dynatrace Assist is built for exactly that. It works alongside you, sharpening decisions, removing friction from investigations, and accelerating the steps that typically slow teams down.

Let’s look at the following example: when a production issue arises, teams waste precious minutes jumping between dashboards, logs, and alerts to reconstruct what went wrong and determine the next steps.

Investigations start with a simple question like “Summarize all open problems and highlight those that need remediation.” Assist immediately pulls together the evidence. It connects Grail data, Smartscape relationships, and real-time signals from Dynatrace agents to piece together a clear explanation of the issue. Instead of scattered clues, Assist collaboratively guides you in selecting a problem to focus on and recommends how to remediate it. This continuous flow compresses investigation time, reduces MTTR, and gives teams a controlled way to move from insight to remediation without breaking focus.

Explain raw data with actionable insights

Logs, signals, and low‑level data points often require deep domain knowledge to interpret. In large digital environments, that knowledge is spread across different teams, which means understanding what a particular message means, how serious it is, and whether it requires action can easily slow teams down.

When you select a log entry (or another signal in Dynatrace) and ask Dynatrace Assist to explain it, it responds like an expert colleague with the necessary domain knowledge and an understanding of the system’s inner workings. It interprets the data in context, highlights what is important, clarifies the potential impact, and outlines likely causes or sensible next steps. Instead of searching for error codes elsewhere or waiting for someone with deeper expertise, teams get immediate clarity. This turns raw, technical signals into practical guidance and shortens the path from confusion to confident action.

Identify and respond to critical vulnerabilities

Vulnerabilities in your system significantly increase the risk of critical incidents, such as data loss or unauthorized access. When a new vulnerability alert appears, teams need immediate clarity about what it means, which services are exposed, and what actions they should take.

With a series of simple prompts, such as described below, Assist pulls in security findings, maps the impacted components across Smartscape, and identifies the root cause. It guides you through the decision-making process, including proposing a fix to the identified problem. All without leaving Dynatrace or switching between tabs, applications, or tools. This tight loop gives teams a faster understanding, more consistent responses, and a significantly reduced risk window.

Prompts used in this use case:

1. Are there any open threats I need to be aware of?
2. Investigate the SQL injection vulnerabilities and provide more details.
3. Show me input for <<add your vulnerability>>.
4. Give me a fixed query.

Dynatrace Assist is here, and it’s just the beginning

Dynatrace Assist doesn’t replace your expertise; it amplifies it, so you can resolve issues sooner, uncover insights more naturally, and move from intention to outcome with far less effort. It is your gateway to Dynatrace Intelligence, which makes you and your teams more productive, accelerates your operations, and uses the full Dynatrace platform to deliver real outcomes.

Ask. Analyze. Act.

And start today with Dynatrace Assist.

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Agentic AI is no longer a concept—it’s a catalyst for enterprise transformation. According to the Boston Consulting Group, organizations that embed AI agents into workflows, rather than simply bolting them on, are achieving 30–50% faster processes and reducing low-value work by up to 40%. These gains come when autonomy is balanced with human oversight and governance from day one. Dynatrace is facilitating this shift through an agentic ecosystem that connects trusted, real-time production context to AI agents across development, operations, and security.

AI is only as smart as the data it works with and only as effective as the workflows it’s embedded into.

Agentic ecosystem as part of the agentic operations system

With the launch of Dynatrace Intelligence, the Dynatrace platform has evolved into an agentic operations system that provides autonomous, intelligent collaboration across development, operations, and business workflows. It’s uniquely architected to power real-time, autonomous operations by orchestrating both ready-made Dynatrace agents and external ecosystem agents.

Whether it’s invoking a coding agent, optimizing your cloud or Kubernetes infrastructure, or creating a ticket, Dynatrace Intelligence coordinates bi-directional interactions between agents in the agentic ecosystem, including AWS Kiro, GitHub Copilot, ServiceNow Assist, Azure SRE agent, Atlassian Rovo Ops, and many others.

A trusted ecosystem is one where agents and AI assistants in your IDEs, ITSM suites, and other tools can securely connect to Dynatrace to interact with real-time observability and production data in context (metrics, logs, traces, topology, vulnerabilities), reason with causal intelligence, and take actions based on smart workflows—all under your supervision, and designed to achieve the goals you define.

Ecosystem architecture

Our three-layer architecture provides precision, governance, and extensibility, allowing teams to innovate without compromising control.

• Grail®, the unified data lakehouse, and Smartscape®, Dynatrace’s real-time dependency graph, provide the technical foundation, analyzing dependencies across business, teams, services, processes, infrastructure, and more. They ensure that Dynatrace Intelligence acts based on facts, not guesses, and ensure its AI-powered decisions are accurate and actionable, as a prerequisite for autonomous operations.
• The Dynatrace MCP server provides connectivity to the Dynatrace platform as well as the necessary tools for interacting with Dynatrace Intelligence to automate tasks, triage problems, perform risk assessments, or update business workflows.
• Agentic workflows, ready-made agents, and ecosystem agents address real-world use cases: from crash inspection to vulnerability remediation, Kubernetes optimization, or improving developer productivity.

Real agentic ecosystem use cases for developers, SREs, and IT Ops engineers you can implement today

The selected examples below show how AI‑powered, production‑aware workflows remove friction and accelerate real outcomes across development, operations, and security. For more scenarios and use cases showing what you can already unlock with our agentic ecosystem, explore our Dynatrace agentic ecosystem integration blog posts.

AI-accelerated troubleshooting and development in your IDE

Developers often lack immediate access to deep production context when debugging or optimizing services. Moving between terminals, dashboards, and monitoring tools interrupts flow, slows investigation, and forces teams to rely on partial information. This creates delays in identifying issues, verifying changes, and understanding real performance impact, wasting time to understand what’s happening in production. A single incident forces them out of their IDE and into complex tool‑hunting, increasing cognitive load and slowing onboarding and MTTR.

The Dynatrace integration with Amazon Kiro brings real‑time observability directly into the developer’s terminal. Using natural‑language prompts, developers can instantly retrieve live metrics, logs, traces, problem context, and topology insights from Dynatrace without leaving Kiro. This eliminates context switching, accelerates diagnosis, and allows rapid decision‑making in the exact moment developers need clarity. With production‑accurate insights available inline, teams ship fixes faster, understand impact sooner, and maintain high development velocity with minimal friction.

Or use Dynatrace with GitHub Copilot to bring real‑time production insight—logs, traces, root‑cause details, and security context—directly into VS Code. Copilot retrieves precise Dynatrace intelligence through natural‑language prompts, helping developers diagnose issues, validate vulnerabilities, and verify builds without ever leaving their coding flow. The result is immediate troubleshooting, dramatically reduced context switching, faster fixes, and a smoother, more productive development experience.

AI‑assisted SRE operations with autonomous cloud investigations

SRE teams are under constant pressure to maintain reliability across increasingly complex, distributed cloud environments. When a degradation occurs, they must manually trace symptoms across layers, correlate signals from multiple cloud providers, and determine whether the issue is caused by code, infrastructure, configuration drift, or external dependencies. This slows down detection, clouds impact assessment, and leads to long, expensive recovery cycles, especially when incidents span AWS, Azure, or hybrid environments.

Dynatrace integrates with both the Azure SRE Agent and the AWS DevOps Agent to deliver autonomous, context‑aware cloud investigations. When an issue emerges, these agents surface precise, real‑time intelligence from Dynatrace, such as causal root‑cause analysis, dependency graphs, impact radius, service health, and cloud resource anomalies, all directly where SREs work. The agents evaluate the situation, propose remediation steps, and, when approved, trigger automated fixes using Dynatrace workflows or native cloud actions. This eliminates the slow, manual stitching together of cloud and observability data, accelerates root‑cause identification, and allows for faster, safer recovery. SRE teams gain clear, actionable insights within seconds, reduce MTTR dramatically, and advance toward autonomous operations across multi‑cloud environments.

AI‑powered incident management with real‑time production context

Most incident tickets arrive with almost no meaningful context—just a timestamp, a vague description, or a user complaint. Engineers don’t know the severity, which systems are impacted, or what caused the issue. They waste precious time switching between monitoring tools, dashboards, and logs just to piece together the basics. This slows response times, drives up costs, and keeps MTTR far higher than it should be.

By integrating Dynatrace with Atlassian’s Rovo Ops agent, you get real‑time production insights directly into Jira Service Management. Dynatrace automatically detects problems, ties them to their causal root, maps dependencies, and understands impact across the entire ecosystem. This context is then surfaced directly in Service Manager, with no tool‑switching required. Engineers instantly see what’s happening, who’s affected, and the actions needed to resolve the issue. The result is dramatically faster diagnosis, smarter remediation, and a significant reduction in MTTR—all powered by live, trustworthy production context delivered exactly where teams work.

AI success is about people and processes

Adding agents to legacy processes won’t deliver meaningful outcomes. Successful agentic AI projects require real transformation, not just technology adoption. Such a transformation needs a strong and trusted foundation. A platform that provides trusted, contextual data ready to support enterprise-level requirements for agents to make the right decisions and act intelligently, and trusted ecosystem partners.

Together, Dynatrace provides reliable agentic AI-powered observability, helping organizations build more resilient applications and deliver better customer experiences.

How to get started

Identify a developer or small team to get started. Connect your MCP client with the Dynatrace MCP Server, get inspired by recent agentic ecosystem blog posts covering use cases for developers, SREs, and IT Ops engineers, or have a look at our documentation to learn more.

Go to Dynatrace Hub to connect to Dynatrace MCP server.

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The whole industry is using and adopting agentic AI. In fact, AI agents are only as effective as the data that powers them. Whether supporting developers through code assistants, accelerating ITSM workflows, automating cloud operations, or enhancing threat detection, you need high-quality data with full context awareness. Only this allows AI to be a trusted and effective partner, delivering reliable recommendations based on a deterministic foundation. The Dynatrace MCP Server, as part of Dynatrace Intelligence, allows AI assistants to interact with Dynatrace and access live, context-rich observability data and reliable AI-insights for smarter, autonomous operations.

Get real-time insights from your digital systems right inside your processes

In traditional IT monitoring, fragmented data and manual tagging were inconvenient. In an AI-native world, they’re a fundamental flaw. AI systems built on incomplete or disconnected data can’t make informed decisions, leading to unreliable recommendations or, even worse, unreliable autonomous action—a scenario that keeps executives on edge.

Dynatrace Intelligence, the agentic operations system built into the Dynatrace platform, solves this with a real-time, deterministic, and context-rich understanding of your entire digital systems.

The Dynatrace MCP Server, as part of Dynatrace Intelligence, provides a secure and governed interface that allows your AI ecosystem to interact with the Dynatrace platform and access data and findings powered by Dynatrace Intelligence. It uses the Model Context Protocol (MCP), an open standard that defines and handles safe interactions between agents, external data sources, and tools.

Together, Grail and Smartscape provide the technical foundation for Dynatrace Intelligence to let agentic AI act on facts, not guesses, ensuring that AI-powered decisions are accurate, scalable, and actionable, a prerequisite for reliable autonomous operations.

By exposing Dynatrace’s unique capabilities, the MCP Server delivers trustworthy, real-time knowledge directly into agentic workflows. This includes:

• Contextualized signals with a holistic understanding of every data point within its full operational and business context, provided by Grail, Dynatrace’s AI-optimized, unified data lakehouse.
• Real‑time topology and causal dependencies, powered by Smartscape, a real-time dependency graph that reveals how systems, services, and cloud components relate and influence one another.
• Deterministic, causal‑AI‑driven root‑cause analysis with correlation to real business impact.

Empower your AI agents to deliver greater value through live production data

You can connect Dynatrace to any MCP client in minutes: no server to deploy, install, host, or maintain. Use the Dynatrace MCP Server to accelerate integration and ensure a smooth transition from pilot to enterprise-scale adoption.

Through the tools hosted on the Dynatrace MCP Server, you can use natural language to query all your data on Grail, check system health, and get problem analyses and remediation recommendations. This allows seamless access to production insights across the applications you already use, including your IDE, Microsoft Copilot, Slack, and automation platforms like n8n. The MCP Server powers numerous more integrations: Azure SRE, AWS DevOps, GitHub Copilot, Atlassian Rovo Ops, AWS DevOps Agent, Kiro, and Amazon Q, to name just a few.

For customers exploring their own tailored solutions, the community-driven local MCP Server is also available. It already sees wide adoption as a flexible way to experiment with new use cases, adapting the MCP to specific needs, and prototyping ideas before moving them into production.

Where the MCP Server delivers value

The Dynatrace MCP Server brings production truth directly into development, operations, ITSM, and business workflows, allowing AI assistants to reason, decide, and act with full contextual awareness. Examples include:

• Development workflows: In-IDE access to live production signals, root causes, exceptions, impacts, and even code-level stack traces, for example, GitHub Copilot querying Dynatrace data in natural language to validate changes and accelerate fixes, with no tool switching required.
• Operations & SRE: Real-time context for accelerated triage, proactive remediation, and evidence-rich postmortems, for example, the Azure SRE Agent integrates with the MCP Server to diagnose anomalies and trigger automated responses.
• ITSM & Incident Management: Enriched tickets with topology, dependencies, business impact, and AI-driven recommendations, for example, Atlassian Rovo Ops delivers end-to-end, context-aware incident management.
• Business Intelligence: Live production insights for product, sales, support, and process teams to link system behavior to customer and business outcomes, provided through MCP clients like Slack, Microsoft Copilot, or ChatGPT.

The Dynatrace MCP Server is now generally available

Dynatrace Intelligence provides insights grounded in real-time topology, deterministic causal analysis, and deep semantic understanding of your environments. Every recommendation is rooted in precise, explainable, and actionable production truth, giving AI systems the reliable foundation they need to operate safely and autonomously.

The Dynatrace MCP Server brings contextualized observability data and insights from Dynatrace Intelligence directly into your broader AI ecosystem.

Connect Dynatrace to any MCP client and start using it in minutes.

You can work with widely available MCP clients such as Microsoft Copilot or ChatGPT, integrate the Dynatrace MCP Server into your ITSM and incident management workflows through ServiceNow or Atlassian Rovo, bring production context directly into VS Code or other IDEs, or orchestrate automated actions through workflow tools like n8n or Copilot Studio.

Explore real-world examples in our latest blog post on the AI ecosystem, or explore all Dynatrace AI ecosystem-related announcements.

For details on configuring the Dynatrace MCP Server, go to our documentation.

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Imagine commissioning le Carré and Fleming to build your perfect undercover agent: quietly embedded in the system you’re watching. You hand in your mission brief, which includes the target, objective, and behaviors to track. Your agent observes without drawing attention, reporting insights back to you. On cue, the information flow you’ve carefully orchestrated turns signals into actionable intelligence that helps pre-empt risk.

Dynatrace doesn’t write spy fiction. However, even better, Dynatrace now lets you write your own smart agentic workflows that deliver intelligent reports and react to changes in your environment based on your objectives.

Adding generative AI to your workflow

Using the power of gen AI, Davis CoPilot® transforms your workflows into agentic instructions. Davis CoPilot lets you explore data using conversational language, translating complex data and queries into summaries, and provides intelligent recommendations across Dynatrace.

Integrated into Dynatrace Workflows, Davis CoPilot is your toolkit for building smart automations, bringing the power of generative AI into your mission-critical workflows.

By embedding Davis CoPilot in your workflows, you can associate any automation with any number of conversational automations. Your workflows can even perform actions autonomously when combined with precise Davis® AI forecasting, for instance, scaling resources based on forecast demands.

In real time, these workflows monitor live data, summarize critical issues, and identify remediation paths or emerging threats. When scheduled, these smart workflows help you outsource routine tasks, such as alerting stakeholders of costly queries.

Let’s look at some examples of how these smart workflows can help you in your daily work.

Build agentic workflows that respond to critical events

Proactive guiding through complex problem remediation

Let’s assume you want to build an automation that cuts through alert noise and analyzes a problem as it occurs, guiding you through the remediation. When a new problem is detected, Davis CoPilot extracts the problem details, summarizes the situation, and provides tailored remediation guidance. By embedding it into a smart workflow, you can select your preferred automation to automatically syndicate this information, populate a ticket in ServiceNow or Jira, or post it to a dedicated Slack channel.

See how you can set up a workflow automation that automatically sends summaries and remediation guidance when a new problem is detected.

Monitor emerging threats to help you orchestrate a response

Next, you can build an agentic workflow that helps you monitor emerging threats and assess their risk to your environment as vulnerabilities are detected in your tenant. In plain language, you instruct your agent to extract IOCs, query security events in your environment, and correlate them with observability data in your environment. Information provided by the external threat feed is automatically matched against the live context in your tenant. The agent has now collected all the necessary information and provides a reliable risk assessment, along with a plan to orchestrate a response, directly in your Slack channel, ensuring around-the-clock visibility and a rapid response.

Write the future: Build an agentic workflow that autonomously auto-scales your resources

Dynatrace helps you build agents that reason autonomously. The key is to deliver data as precise as Dynatrace forecast capabilities. In this example, we linked the power of Davis AI to forecast demand, with generative AI and GitHub automations. Davis AI predicts the number of resources the hyperscaler infrastructure will need based on forecasted demand. When Davis AI notices a scaling need, Davis CoPilot interprets the data and autonomously edits the manifest using the GitHub automation. Giving you one end-to-end workflow that automatically scales resources up or down based on forecasted needs. To see this in action, watch how this workflow autonomously edits a manifest based on Davis AI suggestions to auto-scale a Kubernetes cluster.

Schedule agentic workflows to optimize routine tasks

Do you feel like sleeping in a little later? Maybe stretching your lunch break a little longer? Running that extra hill without sacrificing your productivity? Scheduling Davis CoPilot into your smart workflow is a great way to automate recurring tasks and save time.

Build an automation that predicts resource consumption

A recurring challenge for SREs is analyzing the full environment to predict future bottlenecks or over-resourcing and continuously translating the data to update stakeholders. Even with great observability in place, you need to ensure that you interpret the data and make timely decisions to inform future provisioning.

By combining Davis AI forecasting automation with Davis CoPilot, you can build an agent that answers key questions, such as which workloads are most resource-intensive, which resources show the most variance, and which require frequent scaling. This automation is capable of highly reliable forecasts, even when data points are limited. The automation interprets the data and emails actionable recommendations directly to you and anyone else who needs to stay informed.

To see this in action, watch the section of this video that explores predicting resource consumption.

Smart workflows that optimize query costs

Scheduling tasks can even help you keep costs lean and efficient. For admins or budget owners, staying within financial limits while maintaining performance is a constant challenge. In this example, we built a smart workflow that identifies the top 20 most expensive queries from the last 24 hours. Davis CoPilot analyzes each query and sends optimization recommendations directly to the query authors via email.

To implement this yourself, tailored to the most expensive queries executed on your tenant, go to our documentation

Conclusion: Adapt your workflows to any stage of your automation journey

These are just a few examples; the applications for it are endless. We’ve designed this workflow action to cater to your organization’s automation appetite. You may want to transform how you keep business stakeholders informed about what’s happening in your environment, leveraging Dynatrace’s highly accurate insights, which are translated into plain language and actionable next steps.

Alternatively, you may be ready to transition towards autonomous operations, where automation not only supports but also acts in a controlled and reliable manner. Davis CoPilot embedded into your workflows opens the door to your agentic journey.

Start your agentic journey and join the Davis CoPilot for Workflows Preview

Davis CoPilot for Workflows is available as a Preview. Sign up now and see how generative intelligence embedded into your workflows transforms your automation. Today, it helps you react faster, optimize more effectively, and collaborate seamlessly. Tomorrow, it will go even further: anticipating needs, orchestrating actions, and enabling truly autonomous reasoning.

The post Write the future: Create your own agentic workflows appeared first on Dynatrace news.

Ninety-five percent of Generative AI projects fail to deliver measurable value, and leaders are under mounting pressure to demonstrate that their AI investments are effective. Achieving this requires clear visibility into how and where AI is used, and the outcomes it’s driving.

Dynatrace is setting a standard for observability across the AI stack, and we’re extending that same level of insight to our own AI tools. The new Davis CoPilot® Feature Adoption Dashboard utilizes the same telemetry that Dynatrace teams rely on to improve product quality. Assess effectiveness and optimize how Davis CoPilot supports productivity and decision-making, to move from experimentation to sustainable results.

Davis CoPilot, the Dynatrace platform’s LLM-powered assistant, helps teams work faster by leveraging the full context of their data on Dynatrace to deliver precise and actionable answers. The result is less time spent searching for information or onboarding users, and more time extracting the maximum value from the Dynatrace platform and achieving measurable outcomes.

IT and central team leaders typically offer Davis CoPilot to their users, with specific goals in mind that align with their organization’s broader AI strategy. Initiatives like these typically aim to achieve three key objectives:

• Adoption and engagement: Ensure AI becomes an integral part of routine workflows, so value can scale across teams.
• Productivity gains: Reduce manual effort, increase speed to insight, and improve the quality of outcomes.
• Demonstrable business value: Connect usage to measurable results, such as reduced operational costs, faster incident resolution, or improved service levels.

Understand how AI is used and how it delivers value

To make these objectives measurable, you need visibility into how AI is adopted by your users, the purposes it serves, and whether it delivers the intended value. Only then can you identify where improvements are needed. The ready-made Davis CoPilot Feature Adoption Dashboard delivers this visibility out of the box, showing how Dynatrace generative AI features are used across your organization. Based on the provided metrics and insights, administrators and central teams can make data-driven adjustments.

Know how frequently and for what purpose Davis CoPilot is used, in real time

Gain real-time visibility into when and how regularly teams are using Davis CoPilot in their workflows. The dashboard highlights active engagement, query activity, and usage trends across your organization, helping you understand where Davis CoPilot delivers the most value and where additional enablement may be needed.

By analyzing usage patterns, you can identify high-performing teams, monitor overall adoption progress, and ensure employees are using Davis CoPilot effectively to achieve meaningful outcomes.

For a deeper analysis, break Davis CoPilot usage down further by skill:

• Chat: Analyze chat interactions and workflow actions (currently in private preview), showing how users engage with Davis CoPilot to ask questions, troubleshoot issues, and automate routine tasks.
• Natural language querying: Tracks how users convert everyday language into Dynatrace Query Language (DQL) commands, supporting faster data exploration for both technical and non-technical users.
• Explain DQL queries: Shows how users rely on Davis CoPilot to interpret and summarize complex queries, making it easier to understand and build on existing work.
• Document search: Tracks how users engage with AI-driven document retrieval for accelerated troubleshooting in the Problems app.

Track user experience and satisfaction

To determine whether Davis CoPilot delivers value, it’s important to measure not only usage but also the quality of user interactions and outcomes. The dashboard tracks execution times and success rates to demonstrate how well Davis CoPilot performs in real-world scenarios. This makes it easier to identify technical issues such as invalid DQL generation or prompts blocked by guardrails and content filters.

Additional user feedback adds context to these signals. Thumbs-up and thumbs-down reactions help indicate where users achieve the desired outcome and where they run into problems. When negative feedback clusters around similar prompts or skills, administrators can examine the failed prompts, identify common failure modes, and understand the conditions that lead to them. This supports targeted follow-up actions, such as improving internal guidance for AI usage, reinforcing enablement for specific teams, and surfacing actionable improvement requests to Dynatrace.

For example, if multiple users struggle with natural language queries for Kubernetes data, admins can review the failed prompts, provide best practices, and verify that these measures lead to higher success rates over time. You can even consider enriching your data by adding common synonyms with OpenPipeline. Nequi shared their story at Perform 2025.

Together, operational metrics and contextual feedback help organizations to quickly identify friction points and take concrete steps to improve user outcomes and overall satisfaction.

Optimize performance of AI-generated insights

The dashboard also provides transparency into the queries executed through Davis CoPilot, including query counts and the volume of data scanned. This helps you better understand the resource and cost impact of AI-generated insights across your environment. This level of visibility is critical, as many AI initiatives stall because teams lack the insight to understand the operational impact of increased usage.

By identifying data-intensive queries early, you can optimize performance, control cost exposure, and avoid unexpected resource spikes that can undermine confidence in scaling AI. Capabilities such as segment filtering or organizing data into dedicated buckets allow you to fine-tune data access based on organizational needs. This gives you the ability not only to monitor AI activity but also to adjust and govern it responsibly, ensuring Davis CoPilot remains efficient, controlled, and aligned with your broader business objectives.

Make use of the full potential of Davis CoPilot

The Davis CoPilot Feature Adoption Dashboard equips you with the insights needed to scale Davis CoPilot responsibly, maximizing productivity gains while maintaining control. With clear visibility into usage, success rates, and operational impact, you can build a stronger foundation for continued AI expansion.

The dashboard is instantly available in the environments of Dynatrace customers who have enabled Davis CoPilot.

The post Enhance the impact of Dynatrace Davis CoPilot with built-in observability appeared first on Dynatrace news.

Deploying and safeguarding software services has become increasingly complex despite numerous innovations, such as containers, Kubernetes, and platform engineering. Recent global IT outages, such as the CrowdStrike incident, remind us how dependent society is on software that works perfectly.

Organizations strive to strike a delicate balance between cost, time to market, and innovation. This challenge is more pressing than ever as businesses seek to stay competitive while ensuring their software remains robust and secure.

This necessitates a comprehensive platform that empowers enterprises to understand IT and software within the broader context of their business operations, giving them confidence that their software and IT infrastructure are reliable.

Scale with confidence: Leverage AI for instant insights and preventive operations

Using Dynatrace, Operations, SRE, and DevOps teams can scale efficiently while maintaining software quality and ensuring security and reliability. Its AI-driven exploratory analytics help organizations navigate modern software deployment complexities, quickly identify issues before they arise, shorten remediation journeys, and enable preventive operations.

We’ve added numerous enhancements to our platform, leveraging advanced AI and automation for smarter software observability.

In this blog post, we show you how to

• Get AI-driven insights directly on your operations dashboards
• Improve MTTR with AI-assisted problem analysis and logs and traces in context
• Leverage Gen AI through Davis CoPilot to get insights into root causes
• Automate remediation of AI-detected problems with simple workflows
• Adopt Preventive Operations with AI forecasting and automated action

Get AI-driven insights directly on your operations dashboards

A high-level, customizable view of your data is crucial in modern software operations. Dynatrace Dashboards, powered by Grail™ data lakehouse and Davis® AI, offer precisely that. They provide a comprehensive overview, seamlessly integrating health and problem-related information into a single view. You can chart your topology across data silos alongside all alerts, events, and problems using honeycomb tiles, which offer convenient drill-downs into the problem-debugging user flow.

Dynatrace ensures that context is seamlessly integrated into the platform, thus simplifying complexity for you as a user when analyzing issues and allowing you to focus on what truly matters. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act. This approach not only improves user experiences, it ensures that critical insights are accessible to both experts and novices. By simplifying remediation journeys and extending features to more user groups, Dynatrace enables results across all teams.

When a truly important issue stands out, the next step is refinement. With a few clicks, you can segment and filter your data to focus on specific applications, assignment groups, or regions. Directly mapping and surfacing ownership information within data segments accelerates incident assignment notifications and triggers automatic remediations.

If you see an issue or need to look closely at a specific application where an issue was identified, simply select the element to be seamlessly directed to the Problems app. There, you can dig deeper while continuing to focus on your selected segment. This tight integration, following a golden thread of insights, ensures that you’re more productive. To experience the possibilities of AI-empowered dashboards, try our example dashboard on the Dynatrace Playground.

Improve MTTR with AI-assisted problem analysis, logs, and traces in context

The Problems app delivers opinionated AI-assisted problem analysis optimized for Operations and Site Reliability Engineers (SREs) and developers. According to IDC, guiding users visually and automatically surfacing all critical details enables a 56% faster mean time to repair (MTTR) for critical incidents.

When a large-scale incident occurs, follow the red flag that Davis AI uses to identify the root cause, pinpoint all relevant details, and visually reproduce the details in charts, highlighting the affected deployment.

Besides identifying the root cause, Davis AI also automatically connects all relevant log lines. Logs are invaluable for identifying further insights and detecting fundamental flaws, such as process crashes or exceptions. With a single click in Problems, all incident logs are surfaced automatically. But we don’t stop there, Dynatrace also seamlessly integrates relevant trace data, offering full visibility into even complex, microservices-based architectures.

By providing these end-to-end insights, Dynatrace and Davis AI empower SREs, developers, and architects to quickly dive deep into an incident’s details, including all relevant logs and traces. Using this context, they can effectively focus on fixing and remediating code-level issues, significantly improving MTTR, and ensuring that critical incidents are resolved swiftly and efficiently.

Leverage GenAI via Davis CoPilot for insights into root causes

Dynatrace offers precision tools for domain experts to solve complex problems and dig deeper into their data. While product owners often focus on the intricate technical details of an incident, they often prefer a quick summary of what happened and what caused it. The soon-to-be-globally available Davis CoPilot™ bridges this gap by summarizing problems and their root causes and suggesting remediation steps based on these insights.

You’re not limited to one problem; Davis CoPilot can simultaneously analyze multiple problems, draw conclusions about their relationships, identify the common root cause, and propose corrective steps. Instead of relying on a team of experts and waiting hours for insights, Davis CoPilot helps you identify similarities and draw relevant conclusions independently and efficiently.

The use of generative AI adds significant value by augmenting Dynatrace-detected technical root causes with knowledge from the global tech community. Generative AI can access and synthesize vast amounts of information from various sources, providing a broader context and deeper insights. This ensures that your teams benefit from the latest advancements and solutions, enhancing their ability to resolve issues effectively and efficiently.

Automate remediation of AI-detected problems with simple workflows

To automatically remediate Davis AI-detected problems, Dynatrace leverages powerful Workflows. Dynatrace workflows can be triggered by any problem or alerting event, automating domain-specific tasks to take remedial actions.

For example, workflows can scale up capacity to adapt to demand or automatically restart a service in case of a crash. With a large catalog of available workflow actions, you can react efficiently to AI-detected problems, reducing mean time to repair (MTTR) by automatically remediating issues.

But you can do much more with it: The recently introduced Simple Workflows, which are included in your Dynatrace subscription with no extra cost, offer greater flexibility and power than standard notifications. You can use the same mechanisms and trigger types to notify your developer team via Slack, create a JIRA issue, or send a PagerDuty alert.

This ensures that your operations, SRE, and DevOps teams can focus on more strategic tasks while the system handles routine problem resolutions. Automation enhances operational efficiency and ensures that your systems remain robust and reliable, even in the face of unexpected issues.

Adopt Preventive Operations with AI forecasting and automated action

Going beyond reactive problem detection, analysis, and remediation, Dynatrace can also leverage predictive AI to anticipate and avoid critical situations before they occur. Using Davis AI forecast, you can easily predict future capacity demands. Combining this knowledge with workflows allows you to take proactive measures to ensure system stability and performance.

Let’s have a look at a concrete example:

It’s easy to predict key indicators of your application, such as order levels or service request counts. Once load and demand rise and Davis AI identifies a potential future issue in your infrastructure setup, Davis CoPilot can automatically generate an updated Kubernetes configuration script for you and automatically upscale the environment to meet future demand. This ensures that your system scales appropriately to handle the anticipated demand, preventing incidents before they occur and eliminating the need to generate a problem.

That’s what we call Preventive Operations. Instead of sending an alert and notifying people, Dynatrace simply fixes the issue. According to Gartner’s Analytics Maturity Model, using predictive AI can significantly reduce the likelihood of incidents by taking preemptive action and remediation.

Start using Davis AI to analyze your environments and predict and address potential issues in advance. This will empower your teams to avoid potential problems and ensure a smooth, uninterrupted user experience.

Tackle business challenges with confidence

Ensure your software runs securely and reliably with Dynatrace and Davis AI.

Dynatrace and Davis AI support you by running your software securely and reliably. This includes advanced root cause analysis, deep insights into detected issues, and corrective actions—whether manual or automatic—to prevent outages before they occur.

Get started

For more information, have a look at our documentation or explore the available resources on the Dynatrace Playground to experience some of these enhancements first-hand:

• Investigate our Problems dashboard or have a look at the Problems app on the Dynatrace Playground
• Experience the Davis AI forecasting capabilities embedded in Dashboards
• Watch a step-by-step video and learn how to proactively scale out your Kubernetes environment
• Dig deeper into our documentation for Davis CoPilot, Problems, or Simple Workflows

The post Powerful exploratory analytics for AI-driven insights appeared first on Dynatrace news.

We’re excited to announce that Davis CoPilot Chat is now available across the Dynatrace platform. Davis CoPilot™, launched in October 2024 to support Dynatrace users with access to their data, now extends across the platform, streamlining user onboarding and providing comprehensive support and contextual insights from various Dynatrace® Apps. With the new Davis CoPilot conversational interface, users can leverage natural language to quickly get answers to their questions, making it easier than ever for users to interact with Dynatrace.

Intuitive access to information boosts team productivity

We understand that taking advantage of the numerous features and functionalities offered by platforms like Dynatrace can be challenging. To help you navigate this and boost your efficiency, we’re excited to announce that Davis CoPilot Chat is now generally available (GA). This new feature provides information and guidance exactly when and where you need it, making your Dynatrace experience smoother and more efficient.

Davis CoPilot leverages the power of generative AI to answer your questions through a globally accessible chat interface. We’re proud to say that Davis CoPilot is multilingual: you can ask questions and get answers in many different languages, including French, Spanish, German, Portuguese, Chinese, Japanese, and, of course, English. Davis CoPilot provides immediate, accurate responses, eliminating the need for extensive searches and reducing dependency on support channels. This makes knowledge more readily available and boosts productivity and user experience for both new and experienced users.

Davis CoPilot Chat follows our recent announcement of the general availability of Quick Analysis in Notebooks and Dashboards, which makes data accessible to technical and non-technical users alike. This means you can interact with data stored in the Dynatrace Grail™ data lakehouse just by using natural language.

Simplify onboarding and quickly find what you’re looking for with Davis CoPilot

You can start using the Davis CoPilot conversational interface immediately. Simply enable Davis CoPilot and assign the relevant user permissions, and the Davis CoPilot button will appear in the Dock.

Start a new conversation with Davis CoPilot Chat by selecting it in the Dock or by pressing CTRL/CMD + I and entering your question.

New users can quickly get up to speed with Dynatrace by asking Davis CoPilot for help with basic commands, setup instructions, and troubleshooting tips. This reduces the learning curve and enables new users to become productive faster. The conversational interface provides step-by-step guidance, making the onboarding process smoother and more efficient.

If you’re already familiar with Dynatrace, you can rely on Davis CoPilot to provide detailed explanations for a wide range of expert questions related to exploring new use cases, advanced configuration topics, and building custom apps.

Here are some examples of questions you can ask Davis CoPilot:

• Onboarding: How do we start sending OpenTelemetry data to Dynatrace?
• Understanding Dynatrace: What is the difference between an event and a problem in Dynatrace?
• Exploring Dynatrace solutions: How can we comply with the Digital Operational Resilience Act (DORA) using Dynatrace?
• Configuring your environment: How do I set up an alert based on an anomaly detector?
• Developing custom apps: How can I import external table data and visualize it using the Dynatrace App Toolkit?

Get contextual assistance at the press of a button

Davis CoPilot seamlessly integrates into our use-case-specific Dynatrace Apps, offering you contextual insights and guidance at the press of a button. While we plan to release additional contextual app integrations in the coming months, several will be available a few weeks after launch, allowing Davis CoPilot to provide you with insights into:

• Kubernetes warning signals
• Individual problem details and the relationships between problems
• Database performance optimization

Simplify Kubernetes: Davis CoPilot decodes warning signals

Understanding the background and root cause of warnings often requires in-depth subject matter expertise. That’s why we integrated Davis CoPilot into Kubernetes. Instead of manually looking up error messages, Davis CoPilot translates warning signals into clear, understandable language. In addition, Davis CoPilot offers a list of typical root causes and related remediation steps. This way, newcomers can quickly become proficient, and experts can elevate their expertise to hero status.

Problems demystified: Davis CoPilot provides insights into root causes

In Problems, Davis CoPilot provides clear summaries of problems, their root causes, and the suggested remediation steps. Davis CoPilot explains individual issues in clear language from the problem details page and can perform a comparative analysis when multiple problems are selected from the list view. This helps you identify common root causes and propose corrective steps without relying on a team of experts and waiting for hours for critical insights. If you want to learn more, have a look at Wolfgang Beer’s latest blog post and learn more about recent advancements in the Problems app.

Optimize database performance: Understand query execution plans

Query execution plans provide detailed information on how a database will execute an SQL query. While these provide the raw data on how to improve query performance and reduce resource consumption, they require expert knowledge to read and interpret. Now, in Databases, Davis CoPilot can provide natural language explanations of execution plans, breakdowns of relevant details, and recommendations on how to improve statement performance. This gives non-expert database users, such as developers, the knowledge they need to optimize their application performance and database utilization.

Empower your teams with Davis CoPilot today

The launch of Davis CoPilot Chat marks the second milestone of our journey. We’re committed to continuously enhancing the assistant’s capabilities with upcoming features, including query explanations, workflow actions, and troubleshooting guides.

Get started with Davis CoPilot today and transform how you and your teams interact with Dynatrace:

• Try Davis CoPilot Chat on the Dynatrace Playground
• Activate Davis CoPilot in your environment and grant your users access.

Thanks for joining us on this exciting journey. We look forward to your feedback and to seeing how Davis CoPilot helps your teams achieve their goals.

Davis CoPilot Chat, as well as the Dynatrace Apps integrations mentioned in this blog post, will be available starting with the release of Dynatrace SaaS version 1.307.

The post Davis CoPilot expands: Get answers and insights across the Dynatrace platform appeared first on Dynatrace news.

Imagine a day when your IT team resolves critical incidents before users even notice them. In today’s multicloud world, complexity is the norm. But what if an AIOps strategy could transform that complexity into your organization’s greatest advantage?

IT operations teams must monitor, maintain, and optimize a broader mix of applications, infrastructure, and technologies, with newer digital business solutions only adding to the strain. To manage it, organizations are turning to AI to help automate tasks such as anomaly detection, root-cause analysis, and incident response. But some IT operations teams are taking this approach a step further, applying multiple forms of AI to accelerate and enhance all business operations.

As AI is evolving, it’s important for organizations to understand the different types of AI and the keys to implementing them to achieve an AIOps strategy that moves from reactive to predictive problem solving.

The three kinds of AI that are key to a successful AIOps strategy

AI has evolved beyond the traditional correlation and probability-based approaches. Now, organizations turn to multiple forms of AI, such as causal, generative, and predictive AI, to manage cloud environments, secure data, and improve business decision-making. Therefore, implementing a successful AIOps strategy requires a deeper understanding of these types of AI.

Causal AI: Think of a global retail chain instantly pinpointing the root cause of a checkout slowdown across thousands of stores. Causal AI uses real-time, contextual data and causal dependencies for precise root-cause analysis and issue prevention. This establishes a business environment safeguarded by automated health monitoring and risk remediation.

Predictive AI: Imagine anticipating server outages hours before they occur, allowing seamless customer experiences. Predictive AI analyzes data patterns and trends, using statistical algorithms and other advanced machine learning techniques to anticipate future system behavior. This means technical users and business leaders can be much more proactive and innovative in the face of constant change.

Generative AI: Consider using generative AI to automate repetitive tasks, freeing up teams for innovation. Generative AI trains on large and diverse data sources, boosting business productivity and efficiency.  But when used in combination with causal and predictive AI and trained on real-time, high-fidelity observability data, generative AI can help organizations accelerate productivity and automate workflows.

The key strategy is using GenAI in conjunction with causal and predictive AI to understand your data and environment using natural conversation, and not as a way to correlate disparate events or anticipate the future. GenAI alone has limitations in these areas.

Implementing AIOps at scale

The multicloud complexity challenge demands a new approach—one that moves beyond toolchains that are stitched together. That’s where Davis AI™ comes in, combining the strengths of three AI capabilities in one: predictive, causal, and generative AI. Davis provides advanced analytics and proactive problem-solving to deliver out-of-the-box insights. Additionally, Davis doesn’t require extensive configuration or integrations. As a result, IT teams and business decision-makers can take immediate advantage of AI and scale it across the organization.

This power-of-three AI and observability approach democratizes the value of complex data analytics for both technical and nontechnical users. By unifying operations, security, development, and business teams with a single, complete, real-time view of activity across every cloud, app, and system, teams have an intuitive, self-service observability environment for problem-solving security and business events. Unified observability and AI put users in control of finding the answers they need from data, without needing to be experts in query languages.

Take your AIOps strategy from reactive to proactive

In addition to unifying teams, Dynatrace enables organizations to cut down on redundant observability tools and vendors, simplifying the overall view and improving insight coherency. And with new standards and capabilities in AI, teams can scale these efforts across the business for a more streamlined, inclusive, and collaborative business approach to resolving operational issues and driving your business forward.

Tool sprawl is an obvious problem for organizations. But choosing the wrong end-to-end observability platform can make things worse—and more expensive. Contact us today to request a demo of the AI-powered unified observability and security platform from Dynatrace and take your first step toward eliminating tool sprawl.

The post How to implement an AIOps strategy at scale appeared first on Dynatrace news.

DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. On average, organizations use 10 different tools to monitor applications, infrastructure, and user experiences across these environments. Such fragmented approaches fall short of giving teams the insights they need to run IT and site reliability engineering operations effectively. Indeed, around 85% of technology leaders believe their problems are compounded by the number of tools, platforms, dashboards, and applications they rely on to manage multicloud environments.

Part of the problem is technologies like cloud computing, microservices, and containerization have added layers of complexity into the mix, making it significantly more challenging to monitor and secure applications efficiently. At the same time, the number of individual observability and security tools has grown. This has resulted in visibility gaps, siloed data, and negative effects on cross-team collaboration. Moreover, teams are constantly dealing with continuously evolving cyberthreats to data both on premises and in the cloud. Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable.

To address this, 79% of organizations are currently using or planning to adopt a unified platform for observability and security data within the next 12 months. But before an organization makes the leap to a unified observability platform, it’s important to examine three essential qualities.

Find and prevent application performance risks

A major challenge for DevOps and security teams is responding to outages or poor application performance fast enough to maintain normal service. Additionally, these teams struggle to tell the difference between important information and false alarms — especially when many hundreds or thousands of notifications pour in at once. Identifying the ones that truly matter and communicating that to the relevant teams is exactly what a modern observability platform with automation and artificial intelligence should do.

Ideally, an observability solution should be able to streamline and simplify technology stacks, enabling organizations to replace multiple tools with a single platform. With AIOps, it is possible to detect anomalies automatically with root-cause analysis and remediation support. It should also be possible to analyze data in context to proactively address events, optimize performance, and remediate issues in real time.

To predict events before they happen, causality graphs are used in conjunction with sequence analysis to determine how chains of dependent application or infrastructure incidents might lead to slowdowns, failures, or outages. This enables proactive changes such as resource autoscaling, traffic shifting, or preventative rollbacks of bad code deployment ahead of time.

Therefore, it’s important to look for an AI-based observability solution that not only predicts and prevents issues but also enhances observability to allow teams to take preemptive action before problems escalate into outages and provide ongoing visibility into service-level fulfillment.

See into cloud blind spots

Versatile, feature-rich cloud computing environments such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform have been a game-changer, enabling DevOps teams to deliver greater capabilities on a wider scale. However, the drive to innovate faster and transition to cloud-native application architectures generates more than just complexity — it’s creating significant new risk.

Expansive multicloud environments are generating disparate data sets and views, making it difficult to see the big picture. Data often lacks context, hampering attempts to analyze full-stack, dependent services, across domains, throughout software lifecycles, and so on. Furthermore, 89% of CISOs say microservices, containers, and Kubernetes have also caused application security blind spots.

One reason for this is it is common for application teams to deploy and utilize services on different clouds to take advantage of those features that best match their use case or familiarity. One study found that 93% of companies have a multicloud strategy to enable them to use the best qualities of each cloud provider for different situations.

In addition to the challenges of managing multicloud environments, DevOps and security teams find it difficult to maintain visibility into cloud-native architectures as Kubernetes becomes the dominant platform for modern applications. Kubernetes architectures enable organizations to quickly and easily scale services to new users and drive efficiency gains through dynamic resource provisioning. Yet, this same dynamic quality is why 76% of technology leaders find it more difficult to maintain visibility into this architecture compared with traditional technology stacks.

To fulfill DevOps and security teams’ need for multicloud insights, observability platforms should enable native data streaming from the major cloud providers for a real-time cloud monitoring experience. It also helps to have access to OpenTelemetry, a collection of tools for examining applications that export metrics, logs, and traces for analysis.

Some observability vendors also provide an agent or client to automate the collection and provide contextualization of telemetry and entity data. With these features working in tandem, teams can perform automated and intelligent root-cause analysis in multicloud and hybrid environments. Further, this approach allows organizations to drive cloud architectural improvements through insights into IT underutilizations and dependencies.

That’s why it’s critical to find an observability platform that can handle the scale and complexity of modern cloud-native workloads while providing continuous insights into the performance and reliability of containerized applications and serverless functions, regardless of where they are deployed.

Get to the root cause of issues

Most AI today uses machine learning models like neural networks that find correlations and make predictions based on them. Correlations informed solely by generative AI are essentially informed guesses or likelihoods of outcomes. This limits their capacity to explain why certain outputs occurred or to make reliable decisions in new situations. AI that relies on large language models (LLMs) is also known to generate answers that may include outdated, vulnerable, or inefficient patterns. According to one survey, 98% of technology leaders said they are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation.

This growing awareness of the limitations of correlation-based AI is driving increased interest and research into causal AI, which aims to determine the precise underlying mechanisms behind events and outcomes. Increasingly, causal AI use cases are enabling organizations to identify the root cause of problems, facilitate remediation, and drive intelligent automation. AI systems that can explain the reasons for their recommendations grounded in causal AI can go a long way in resolving general distrust of AI models.

Integrating causal AI into observability systems can significantly advance an organization’s insight into its environment. Whereas traditional monitoring tools merely alert organizations to issues, causal AI can precisely identify the root cause of performance and quality issues. This leads to quicker and more effective problem remediation, reducing downtime and improving reliability through intelligent automation.

Seek out an observability solution that uses causal AI for critical use cases such as performance degradations, application health, root-cause analysis, resource utilization, auto-remediation, and application security.

The value of a unified observability platform powered by causal AI

Dynatrace offers three essential qualities—proactive incident management, comprehensive end-to-end visibility, and root-cause identification with causal AI—to give a single, complete, real-time view of data. The outcome is faster time to value with automated deployment and discovery. Gains also include greater cost control with no hidden charges or limitations and higher precision with unified operations supported by hypermodal AI capabilities.

The post The keys to selecting a platform for end-to-end observability appeared first on Dynatrace news.

Deal with data overload in the enterprise

In today’s rapidly evolving digital landscape, enterprises are inundated with vast amounts of data. Extracting meaningful insights from this data is crucial for staying competitive. However, traditional data analysis techniques can be time-consuming and demand specialized expertise, limiting how quickly and easily insights can be obtained.

Empower deep data analysis with natural language queries

Davis CoPilot enhances efficiency and productivity by seamlessly integrating generative AI throughout the Dynatrace platform. This feature allows you to effortlessly gain insights and generate queries without needing to learn new syntax or manage complex commands. Consequently, Dynatrace becomes accessible to a broader audience, including non-technical users and those who don’t work with Dynatrace on a daily basis, and empowers teams to make faster data-driven decisions.

Empower users with intuitive data access—without compromising security

At Dynatrace, we recognize the complexities associated with data environments. DQL, the query language employed to analyze data stored in Dynatrace Grail™ data lakehouse, offers remarkable versatility and power, serving as an essential tool for experienced users seeking to fully harness Grail’s capabilities. Davis CoPilot simplifies the data querying process for both professionals and beginners by enabling interactions through natural language. This democratizes data access, allowing all users to generate valuable insights swiftly and effortlessly. Consequently, the data analysis process is accelerated, empowering teams to make informed, data-driven decisions with increased speed and precision.

At Dynatrace, we prioritize the protection of your data. Our solutions are engineered to be secure, reliable, and entirely transparent. Davis CoPilot guarantees that your confidential information is never at risk of being leaked or disclosed across environments, as we ensure continuous protection of your prompts and data. Furthermore, there is no automatic model training or fine-tuning based on your usage, ensuring that your data is employed strictly for its intended purpose—to generate DQL and provide swift insights. This steadfast dedication to security and transparency enables you to use our tools confidently, trusting that your data is well-protected. Look at our documentation to get more insights into the privacy and security aspects of Davis CoPilot.

Get started with quick analysis in Notebooks and Dashboards

Davis CoPilot allows you to perform rapid data analysis in Notebooks and Dashboards by translating natural language prompts into Dynatrace Query Language (DQL). The results are automatically executed and returned, making complex data analysis more accessible than ever before.

Simply create a new notebook or dashboard, then select + Add > Davis CoPilot. Enter your prompt (or try one of our suggestions), and select Run. Davis CoPilot will generate and auto-execute the DQL so you can go from question to data insights in seconds. If you’d rather refine your query before executing it, open the dropdown list next to the run button and select Generate DQL only (this feature is currently only available in Notebooks).

Environment-aware queries unlock full data-context awareness

Davis CoPilot is much more than an AI tool that helps you create queries. Davis CoPilot knows the context of your data, which results in more precise answers using a feature called environment-aware queries.

Having environment-aware queries configured allows Davis CoPilot to identify unique data fields and custom metrics in your environment. You can now run more complex analyses and get better results by crafting more accurate queries that identify and reference relevant entities, events, spans, and metrics straight from your environment. And, of course, we do this without putting you or your data at risk. This functionality is opt-in, and you have full control over which data tables and buckets are accessible to Davis CoPilot. Let’s look at some examples:

If you’re an application owner tracking travel bookings for new trips on a travel website, you’ll likely need to track:

• profit made on each booking  (as a business event)
• applicable discounts (as a business event)
• length of time it takes customers to complete a booking (as a custom metric)

With this in mind, you might give Davis CoPilot the following command: “Show me the average revenue and price reduction for new trips over the last month.”

If you have environment-aware queries configured, the following DQL will be generated automatically, and you’ll get the relevant results you’re looking for.

fetch bizevents , from:now() – 30d 
| filter event.type == “new trip” 
| makeTimeseries interval:1h, {profit= avg(profit), discount= avg(discount)

With environment-aware queries configured, Davis CoPilot infers that “revenue” refers to the profit field and “price reduction” refers to the discount field, even though your prompt doesn’t use the correct field names. However, if you don’t have environment-aware queries configured, Davis CoPilot can’t identify all relevant fields. For example, the following incorrect DQL will be generated if the same conversational command is issued when environment-aware queries are not configured. In such cases, you won’t get any results since the fields mentioned in the command don’t exist in your environment.

fetch bizevents, from:now() – 30d 
| filter event.type ==  “new trip”
| makeTimeseries interval:1h, {avg_revenue = avg(revenue), 
  avg_price_reduction = avg(price_reduction)

Alternatively, you might ask Davis CoPilot the following: “On average, how long does it take customers to book new trips?” If you have environment-aware queries enabled, the following DQL will be generated, and you’ll get the relevant results you need.

timeseries avg(new_trip_booking_duration)

Conversely, if you don’t have environment-aware queries configured, you’ll likely receive an error message because Davis CoPilot can’t correctly map your question to your custom metric key. In this case, Davis CoPilot can’t generate a valid DQL query since it won’t be able to find a matching built-in metric.

User permissions are enforced both with and without environment-aware queries, ensuring that Davis CoPilot provides relevant responses that comply with individual data-access rights. Environment-aware queries truly unlock the power of Grail for everyone in your organization.

What’s next for Davis CoPilot

This is just the beginning of our new AI assistant journey. We’re committed to making Davis CoPilot even better, and we’ve got some fantastic features coming your way, from query explanations to problem insights, document generators, and more.

We value your feedback and are continuously working to enhance our product. Want to share your thoughts? You can share your learnings directly from the Davis CoPilot interface. Your feedback helps us refine the functionality and better meet your needs. You can also request to participate in ongoing or upcoming Preview programs. Get in touch with your Dynatrace account manager if you’re interested.

Get started today and embrace the future of data analytics

The launch of Davis CoPilot marks a significant advancement in data analysis capabilities. If you have a Dynatrace Platform Subscription, Davis CoPilot is available for you with the release of Dynatrace SaaS version 1.301. If you have a classic license, Davis CoPilot is available for you with the release of Dynatrace SaaS version 1.304.

Empower your team with the ability to effortlessly transform natural language prompts into actionable insights. Activate Davis CoPilot in your Dynatrace environment today and explore how it can transform your data analysis workflows.

For more information and to get started, please visit our documentation. Thank you for being part of this exciting journey with us. We look forward to your feedback and seeing how Davis CoPilot helps you achieve your goals.

The post Announcing General Availability of Davis CoPilot: Your new AI assistant appeared first on Dynatrace news.

According to recent research from TechTarget’s Enterprise Strategy Group (ESG), generative AI will change software development activities, from quality assurance to debugging to CI/CD pipeline configuration.

Many organizations are turning to generative artificial intelligence and automation to free developers from manual, mundane tasks to focus on more business-critical initiatives and innovation projects. Therefore, it’s no surprise that generative AI is poised to have a massive impact by automating software development tasks today and in the near term, according to ESG’s data.

In the research, “Code Transformed: Tracking the Impact of Generative AI on Application Development,” sponsored by Dynatrace, findings indicate that AI and automation are already having a major impact on how developers are working today.

Weighing the pros and cons of automating software development

AI-enabled development can eliminate manual effort and free developers’ time to engage in more strategic, high-level code development. Software development tasks include testing and quality assurance (QA), security, coding, debugging, CI/CD pipeline configuration, and documentation.

On the whole, survey respondents view AI as a way to accelerate software development and to improve software quality. According to the survey, 79% of respondents say AI is already helping to reduce time spent on manual tasks.

At the same time, 75% of respondents say it has taken longer than expected to derive value from AI initiatives related to automating CI/CD pipelines.

How AI is reshaping application development

The ESG report explains how three types of AI are reshaping the app development ecosystem:

• Generative AI leverages large language AI models to create new outputs. These help teams with data augmentation, anomaly detection, simulation, and documentation, among other areas.
• Predictive AI uses data collection, algorithm assignment, and model training for user behavior prediction, demand forecasting, fraud detection, and quality control, among other areas.
• Causal AI models the cause-and-effect relationship between variables to help with areas that include personalization, testing, optimization, policy impact assessment, and more.

AI influences QA and container orchestration

Organizations are also using AI for myriad testing and QA activities, including error detection and debugging (44%), among other tasks.

Generative AI is also becoming key to container orchestration—a process that automates the deployment and management of containerized applications and services at scale.

Organizations use generative AI for myriad use cases involving container orchestration, according to the research, such as automated remediation (35%).

Reaping the rewards of generative AI and automation

Ultimately, the report indicates that IT operations (57%) and product development (42%) stand to benefit most from generative AI.

The trend in automating software development tasks, therefore, stands to benefit the stewards of IT systems and product innovation—two central locations of organizational growth and risk mitigation—today and in the future.

Source: Enterprise Strategy Group, a division of TechTarget, Inc. Research Report, Code Transformed: Tracking the Impact of Generative AI on Application Development, February 2024.

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At every organization, the digital landscape is evolving rapidly, presenting IT operations teams with unique challenges.

Teams require innovative approaches to manage vast amounts of data and complex infrastructure as well as the need for real-time decisions. Artificial intelligence, including more recent advances in generative AI, is becoming increasingly important as organizations look to modernize how IT operates.

As a result, organizations are turning to AI to automate tasks—from code development to incident response—to reduce manual effort and human error, and to boost workforce efficiency.

At the same time, challenges remain as organizations aim to become more automated. Some of these challenges involve basic tasks—such as data collection. Others involve introducing new threats as AI becomes more integrated into IT systems as a whole.

In this article, we explore recent survey data from Enterprise Strategy Group (ESG), sponsored by Dynatrace, on how organizations approach IT automation, as well as the benefits and challenges they encounter as they adopt it.

Unleashing automation and AI

According to recent ESG research, 85% of organizations are using, planning to use, or considering artificial intelligence, such as generative, causal, and predictive AI, in many of their functional areas, including IT operations. One could say that AI has moved beyond the “hype cycle” phase and entered a new phase of implementation.

A survey of 360 IT professionals at organizations in the U.S. and Canada involved with observability, IT service management, and IT automation technologies offers insight into the current status and future of AI in IT operations.

AI in production

Sixty percent of respondents indicate generative AI is in production, 54% indicate causal AI is in production, and 53% indicate predictive AI is in production.

Generative AI awareness is most widespread and has an early adoption lead given the popularity of ChatGPT, Gemini, and similar tools on the consumer side, as well as the proliferation of generative AI-enabled natural language querying interfaces. As a result, many organizations are adopting it into production environments.

The heavy burden of collecting and correlating logs

Forty-five percent of respondents find collecting and correlating logs as burdensome or complex.

But organizations still wrestle with even the basics of log management. While respondents have made progress in terms of instrumentation,

This suggests there is ample opportunity for organizations to use a log management and analytics platform such as Dynatrace to ingest and analyze log data. Dynatrace Grail enables organizations to ingest data without predefining schema. Grail, alongside Dynatrace Davis AI, enables organizations to move beyond simple event correlation and to identify the root cause of problems in their applications and infrastructure.

The most likely beneficiaries of generative AI

The top three areas most likely to benefit from generative AI are IT operations (72%), cybersecurity (47%), and application development or DevOps (30%).

Organizations are turning to AI to automate manual tasks and see immediate benefits in IT operations, cybersecurity, and application development or DevOps. For IT operations, this means streamlining resource allocation, automating tasks, and enhancing incident response. For cybersecurity, it means detecting anomalies, strengthening defenses, and evolving alongside emerging threats. And for DevOps, it means accelerating DevOps processes, improving agility, and speeding time to market.

Security remains top of mind

Twenty-seven percent of respondents indicated security vulnerability is a top concern with integrating AI into IT operations.

Traditional and new challenges are emerging when integrating AI into IT operations. Therefore, it’s no surprise that 27% of those surveyed mention security vulnerability as a top concern when it comes to integrating AI into IT operations.

How generative AI improves IT operations metrics

Thirty-four percent of respondents whose organizations use or plan to use generative AI and subsequently measure or plan to measure its value indicate a 31% to 50% improvement in IT operations metrics from generative AI integration in 24 months.

The value of AI in operational acceleration carries tangible value above and beyond incremental features. This acceleration translates to a better return on assets, but it can also increase greenhouse gas emissions, complicating organizations’ ability to sustainably meet acceleration objectives.

To dive deeper into this research, download the free ebook, “Generative AI in IT Operations: Fueling the Next Wave of Modernization.”

Source: Enterprise Strategy Group, a division of TechTarget, Inc. Research Report, Generative AI in IT Operations: Fueling the Next Wave of Modernization, February 2024.

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Today’s macroeconomic environment is dynamic and uncertain, generating many questions about the future of work.

New technologies are disrupting the landscape, while company mergers, acquisitions, and economic volatility abound. As artificial intelligence becomes more pervasive in organizations, the workforce senses that the future of work is undergoing massive shifts.

Proactive workforce members are acclimating to these fluid conditions through a variety of strategies, such as career “zigzagging” (a less linear career path that involves diverse roles), career upskilling, and mentoring.

For women in technology, these strategies have never been more important to help them survive and thrive as they embark on a new era of AI-enabled work, agreed panelists at the “Women in Tech” panel at Dynatrace Perform 2024.

According to Laura Heisman, Dynatrace chief marketing officer and panelist, women should embrace sudden career shifts as opportunities.

Heisman joined Dynatrace in January 2024, with an enduring career to date in the technology sector. She has held positions at Citrix Systems, GitHub, and most recently, VMware. At the outset of her career, she worked in consumer products in Southern California. Then, after being introduced to the “World Wide Web” and new types of businesses and marketing opportunities, she shifted her career course to focus on technology.

“That was a huge zig and zag for me,” Heisman recalled. She compared that moment in her career with the present picture for the workforce, as artificial intelligence matures and has a massive impact on the future of work.

“We are in a similar moment with AI,” Heisman emphasized. “Take the opportunity to learn everything you can. It is this huge moment for all of us in our careers and in how we do our jobs,” she said.

The future of work with generative AI

Data suggests that this inflection point in the future of work—spurred by generative AI (a type of artificial intelligence technology that can produce various types of content, including text, images, audio, and more)—is encouraging excitement and apprehension about job prospects in the era of AI.

According to the report, “Work, workforce, workers: Reinvented in the age of generative AI,” 95% of workers see value in working with generative AI. But approximately 60% are concerned about job loss, stress, and other issues, given the impact of AI on the workforce.

Data also suggests that the workforce is receptive to the coming tsunami of changes AI will bring, particularly in the form of upskilling and reskilling. According to a recent survey, for example, 68% of workers are aware of coming disruptions in their industries and are willing to reskill to remain competitively employed.

That’s why Heisman and other members of the “Women in Tech” panel stressed the importance of continued learning to nurture one’s career. This strategy is becoming essential to thrive in the future of work.

According to Jolly Mishra, director of partner development at Microsoft, it’s also critical for women to encourage the next generation to pursue STEM disciplines (science, technology, engineering, and math)—and to recognize that these disciplines are “cool” for women to engage with—even if younger women are not easily accepted in school as “nerds.”

Inspiring inclusion—and bucking exclusion

Sue Quackenbush, Dynatrace chief people office and panel moderator, asked the panelists about methods to inspire inclusion for women in technology. It sparked a conversation about embracing diverse work styles as key to the future of work.

Indeed, while technology companies often recognize the most assertive people in the room, those with quieter styles may have important contributions to make and that true inclusion is about bringing all good ideas to the table.

“You have to give everyone a voice,” Terese Pate, director of product development at GXC Technology, said. “Sometimes the quietest voice has the best ideas.”

At the same time, panelists noted that inclusion may not always be offered readily, and women still have to steer their own path to be recognized for their talents, not their gender, in the technology sector.

Microsoft’s Mishra recalled trying to pursue opportunities in the sector, then being told, “Don’t waste your time. They don’t accept women in technology.”

Rather than accepting a closed door to opportunity, Mishra said, the lack of inclusion became motivation for her. She was even more determined to pursue the role. “It was essentially a kind of superpower for me,” she said.

Pate agreed. “It was about learning my craft to be as good or better than anyone else in the room. They had to listen to me because I was the one who had the answers.”

Intentional—and unintentional—mentoring

Quackenbush also asked panelists about their mentors during their careers and whether mentorship was a formal process.

Pate noted that she used her entrée into technology as an opportunity to learn from her peers.

Early on, Pate sought out her mentors to teach her and help her elevate her skillset. “I said, ‘Elevate me, work with me. That is huge—and that’s how I learned,” Pate recalled.

Heisman noted that her mentoring process was more informal, with two male mentors who organically served as soundboards and guidance during her career.

“It was not sponsorship, it was mentorship, and it’s a two-way conversation, where we are building a relationship and understanding each other together,” Heisman said. This nontraditional way of thinking about mentorship breaks the barriers of hierarchy and brings parity and equal exchange to the process.

Building a career with grit

Panelists also recognized that pursuing a career in technology requires determination and grit. In some cases, women may need to believe in themselves to pursue worthy opportunities.

Heisman noted that research shows that men will apply to a job with about 60% of the qualifications, while women believe they need 100% of the skills required.  That may discourage them from even applying when in fact they are qualified. Knowing this, job descriptions and hiring methods need to adjust to build diverse teams.

“Be comfortable being a woman in tech. Wear it as a badge of honor. Don’t doubt yourself and your skills,” Heisman encouraged.

But just as the future of work requires persistence, it also brings new opportunities for women in technology that may not have been as rich prior to the emergence of the COVID-19 pandemic.

As Microsoft’s Mishra noted, the normalization of hybrid work has provided all workers opportunities to participate in the sector without following traditional schedules in the office. This flexibility particularly helps women navigating caregiving responsibilities stay in the workforce.

The future of work is about diversity and inclusion

Mishra noted that as the AI era unfolds, diversity and inclusion are approaches that reflect the cornerstone of reliable, unbiased AI. Responsible, reliable AI requires a diversity of data and perspectives to enable information accuracy, freshness, and unbiased outcomes.

Mishra emphasized, “Diversity and inclusion are so important for innovation.”

For all Perform coverage, check out the Dynatrace Perform 2024 guide.

For more on digital transformation and AI, check out our report “The state of AI 2024.”

The post The future of work: How to zig, zag, and steer your career in the AI era appeared first on Dynatrace news.

Artificial intelligence is rapidly transforming the world around us, with applications based on AI emerging in virtually every industry and sector.

This trend has accelerated with the recent democratization of access to generative AI-driven solutions. However, as AI systems become more complex and sophisticated, organizations are learning that they need to ensure the AI they use is responsible and trustworthy.

Data suggests that organizations are quite concerned about the role of AI in making responsible, well-informed decisions. Indeed, according to the recent Dynatrace report, “The state of AI,” 98% of 1,300 technology leaders are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation.

There is an increased focus on trusted, responsible AI because when the following factors are overlooked, they can cause significant financial, business, and legal repercussions:

• The opacity of algorithms. It can be difficult to understand the basis of AI systems’ decisions, particularly when they are trained on large and complex data sets.
• AI system bias. AI systems and their data can be biased, either intentionally or unintentionally, reflecting the biases of their creators or the data on which they are trained.
• Unauthorized usage of data for AI. Every organization needs to carefully consider how to minimize the risk of AI accessing and using data without authorization—and not just a company’s own data, but also customer and user information.

Responsible AI approach at the core

To support a responsible AI approach, organizations need to consider the integrity of their broader strategy for monitoring IT systems. To this end, they need an approach to IT system monitoring that can promote accurate, unbiased, and timely data inputs.

Organizations need an observability platform that can gather, store, and analyze data in a unified manner and retain proper context. This data context becomes the foundation for training AI algorithms with unbiased, accurate, secure, and timely data.

Moreover, a unified approach to observability enables organizations to ensure a responsible approach to AI by providing transparency into how the algorithms arrive at decisions. This enables organizations to ensure that the data insights are devoid of bias and supported by fact-based inputs.

Dynatrace collects and analyzes large amounts of observability and security data. Then, Dynatrace converts this data into precise answers that customers need to simplify cloud operations and deliver flawless and secure digital experiences using a responsible AI approach.

Transparent and explainable AI. Users get full transparency into how Davis AI derives answers and which techniques it has used. Users are in control of each phase of Davis AI processing to ensure data privacy, eliminate bias, and promote fairness.

Trusted data. Customers have full control over the data that Dynatrace Davis AI uses. They can choose which data to share with Dynatrace that Davis AI can use to generate answers. At any time, they can investigate what system data Davis AI is evaluating. This approach gives users the control they need to ensure the data Davis AI trains on and processes.

Data in context. Davis AI makes sure that data is used in the context set up by Smartscape, a real-time, dynamic dependency map that visualizes all application components, and OneAgent, a single agent that provides a set of specialized services that have been configured specifically for your monitoring environment. All the relevant information collected and the associated real-time topology information is put to use.

Causal AI that’s repeatable. Unlike probabilistic approaches, Davis AI delivers causal, deterministic answers that are repeatable—causal AI can identify precise cause and effect. At Dynatrace, we continually test Davis AI to ensure repeatable and reliable results.

Data privacy and end-to-end security. Dynatrace embeds data privacy principles into the core of the platform. This gives customers the ability to extend protections beyond the minimum legal requirements when it comes to protecting customer data. Independent security certifications (FedRAMP, StateRAMP, ISO2700, and SOC2 Type II) and regular independent penetration testing ensure the data security and privacy controls implemented by Dynatrace meet the stringent compliance requirements.

Choosing responsible AI for hundreds of use cases

Dynatrace enables organizations to use the power of AI responsibly to optimize their IT operations. These capabilities can automate tasks, identify anomalies, and make predictions. Dynatrace AI is easy to use and provides actionable insights that can help organizations improve their IT performance. Some of the most common use cases include the following:

• Anomaly detection. Davis AI uses multidimensional baselining to automatically detect anomalies in the response times and error rates of applications and predictive AI to detect abnormalities in application traffic and service load.
• Root-cause analysis. Davis AI automatically detects customer-facing issues and uses topology, transaction, and code-level information to precisely pinpoint a problem’s root cause. This can help organizations to identify potential problems before they cause outages by proactively remedying them.
• Predictive operations. Davis AI can predict when issues will occur, preempt or resolve these issues, and ensure reliable operations. For example, predictive disk resizing and autoscaling resources. The teams can help organizations to prevent outages and to extend the lifespan of equipment.

Trusted AI from Dynatrace is a powerful solution that provides organizations with the control and transparency they need to use AI safely and ethically for building and running resilient software securely.

The post Responsible AI must-haves for unified observability and security appeared first on Dynatrace news.

In the years since the COVID-19 pandemic, organizations have recognized the need to develop greater business resilience.

Companies feel the pressure from myriad macroeconomic factors.

Companies are struggling with rising costs, supply chain imbalances and slowdowns. They have also endured a tidal wave of workforce change wrought by remote work, increasing cyberattacks, and rising geopolitical tensions.

All these factors have increased uncertainty, instability, and rapid change within organizations. Enterprises need greater business resilience to adapt to a dynamic macroenvironment.

Data supports this picture. Organizational disruption is becoming increasingly frequent—and more severe. Indeed, 96% of organizations have experienced business disruption since 2021, according to a PwC survey of more than 1,800 leaders. And 76% of organizations said that the disruption had a medium-to-high impact on operations.

And data center outages are costly.

In the face of this instability and costly disruption, organizations need to develop business resilience to brace for change and remain agile.

“Fostering resilience is not only critical to business performance and transformation, but also ensuring organizations can adapt to virtually any situation,” wrote Vishal Gupta in “Four ways to build a more resilient and future-proof business,” in Fortune.

What is business resilience?

Business resilience is when organizations have systems and processes in place to protect against unforeseen shocks and build organizational agility. In part, business resilience involves an approach to building a technology environment that enables an enterprise to adapt quickly to changing circumstances.

To that end, business resilience requires a strong, secure, and flexible technology foundation to accommodate macroeconomic change.

Certain technologies can support these goals, such as cloud observability, workflow automation, and artificial intelligence. Companies that exploit these technologies can discover risks early, remediate problems, and to innovate and operate more efficiently are likely to achieve  competitive advantage.

Thus, while business resilience is about protecting against unforeseen risk, it also enables an organization to develop a forward-looking strategy that helps it thrive in uncertain times.

“Resilient organizations don’t just bounce back from misfortune or change; they bounce forward,” write Dana Maor, Michael Park, and Brooke Weddle in “Raising the resilience of your organization.” The authors continue, “They absorb the shocks and turn them into opportunities to capture sustainable, inclusive growth.”

To bounce forward, organizations need a strategy to build business resilience. This strategy involves people, process, and technology.

Building business resilience with observability, workflow automation, integrated security, and AI

Technologies such as observability, AI, and workflow automation can boost business resilience by providing insight into complex IT systems in real time. Adopting these capabilities can eliminate manual work and identify cyberthreats that pose risks to data and applications.

Together, these technologies also support business resilience by enabling operational efficiency, cost reduction, and product innovation—all critical business benefits in the wake of abrupt change and disruption.

Modern observability

Observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. When organizations operate in complex cloud environments, they often lack visibility into activity in these environments and how problems arise.

The goal of observability is to provide a comprehensive view of cloud applications, services, and other cloud-based entities so teams can detect and resolve issues. With this kind of comprehensive observability, teams can ensure business resilience with systems that operate efficiently and reliably, and keep customers satisfied.

Workflow automation

Organizations can reduce errors generated by manual workflows and boost team productivity with workflow automation. Manual processes are error-prone given reliance on human input. Manual processes also require substantive effort, which can consume significant staff resources that could be better spent on higher-level, strategic projects.

Workflow automation not only ensures business resilience, it can also help boost productivity and innovative thinking. There are many real-world uses for process automation, including the ability to automatically provision infrastructure—critical for organizations that use cloud architecture—as well as closed-loop remediation capability and the ability to enable teams to collaborate and address problems through targeted notification.

Data indicates that 73% of the leaders in the IT industry attribute some 10% to 50% of time savings by shifting manual tasks to automated ones. and increased efficiency enables teams to become more efficient, strategic, and innovative.

Integrated security and runtime vulnerability management

According to data, 75% of CISOs worry that too many application vulnerabilities leak into applications in production, despite a multi-layered security approach.

A multi-layered approach applies security testing in all stages of development and across devices, applications, networks, and infrastructure.

At the same time, cyber-events such as a zero-day vulnerability can easily penetrate an organization’s defenses. In December 2021, for example, the zero-day vulnerability Log4Shell emerged. It enabled a remote attacker to take control of a device on the internet if the device is running certain versions of Log4j 2.

According to recent data, more than three-quarters (79%) of CISOs say that automatic, continuous runtime vulnerability management integrated with observability is key to filling the gap in the capabilities of existing security solutions. However, just 4% of organizations have real-time visibility into runtime vulnerabilities in containerized production environments.

A platform that can identify vulnerabilities in production and prioritize actions for remediation is thus critical for business resilience as cyberthreats mount and companies risk losing hundreds of thousands of dollars in the days and weeks that follow an attack.

Explainable and transparent AI

As AI models evolve, many organizations are concerned that artificial intelligence algorithms are based on complex, hard-to-understand processes. But for organizations to trust AI-enabled results, the algorithms cannot be based on data that is an impenetrable black box they can’t verify.

Platforms built on “explainable AI” principles not only deliver answers using AI but also enable teams to understand how the platform arrived at those answers, conclusions, and recommendations. Cloud observability that uses fault-tree analysis, for example, can graphically display and map the precise source of error.

As a result, organizations can see exactly how the system arrived at a certain result because the fault tree displays the logic leading to certain conclusions clearly, which boosts business resilience.

“In a world where the speed of response is essential, the organization that uses AI to analyze more information in real time or automate processes to increase its efficiency will be likely to adapt more quickly and thrive,” wrote Mike Etting in the Forbes article “Organizational resilience and operating at the speed of AI.”

Achieving business resilience in times of uncertainty

Ultimately, enterprises are turning to technologies such as modern observability, workflow automation, integrated security, and AI to become more efficient and productive.

Nearly three-fourths (73%) of companies are prioritizing AI over all other digital investments, with the immediate focus on improving operational resilience in an unprecedented environment, according to a study by Accenture.

In the report  “Reinventing enterprise operations,” Accenture determined that 90% of business leaders are applying AI to tackle aspects of operational resilience, which spans data-driven capabilities, such as finance (89%) and supply chain (88%), to experimentation with generative AI.

“All CEOs are under pressure to digitize faster, put more resilience in the business, and find new pathways to growth,” said Yusuf Tayob, group chief executive of Accenture Operations. “The right investments in technology while advancing talent, data and processes is what drives a new performance frontier.”

If your organization is considering modern observability as part of a larger business resilience strategy, start a free trial today.

The post Achieving business resilience with modern observability, AI, and automation appeared first on Dynatrace news.

Hypermodal AI, which combines predictive AI, causal AI, and generative AI, boosts productivity across operations, security, development, and business teams.

This expansion of Davis AI complements the proven Dynatrace predictive AI model (for example, forecasting and anomalies) and our causal AI model (for example, determination of a problem’s root cause, security risks, user impact, and steering automation), which are at the core of the Dynatrace platform.

Davis CoPilot empowers users to effortlessly create queries, data dashboards, and data notebooks using natural language and provides coding suggestions for workflow automation, reflecting the unique attributes of each customer’s hybrid and multicloud ecosystem. It also simplifies and accelerates onboarding, configuration, and adoption of the Dynatrace platform.

What is hypermodal AI, and why is it essential for reliable observability, security, and automation at scale?

Hypermodal AI intelligently combines multiple AI techniques—predictive AI, causal AI, and generative AI—helping organizations effectively solve BizDevSecOps use cases.

Dynatrace applies these techniques to the broadest set of modalities in the market, including the data types of metrics, traces, logs, behavior, topology, dependencies, events, and more, with unmatched precision for precise predictions, accurate determinations, and meaningful insights.

Davis AI transforms and augments data to enable more useful analysis, perform automatic tasks, and respond to user requests:

• Predictive AI uses machine learning (ML) and statistical methods to recommend future actions based on data from the past. Dynatrace uses the various data types across metrics, logs, traces, behavior, events, and more in its Grail™ data lakehouse and causal dependencies from Dynatrace Smartscape® to provide continuous forecasting and anomaly prediction, including cloud application health, infrastructure needs, sales, and customer experience trends, seasonality, and other historical behaviors.
• Causal AI processes observability, security, and business data in the context of causal dependencies from Dynatrace Smartscape topology to precisely determine the needle in the haystack in continuously and dynamically updated software services. It groups anomalies, pinpoints root causes, ranks security risks, enables precise attack investigation, and provides business impact assessments, all automatically. This AI also triggers automated remediation actions. It further enables teams to explore trends or patterns with built-in domain and topology context.
• Generative AI drives productivity through AI-powered analytics and automation for all members of your organization. Davis CoPilot interprets natural language to create queries, dashboards, and notebooks and provides suggested code for automation workflows. It further simplifies access to best practices for observability and security use cases and answers “how-to” questions precisely. It also guides users who want to observe new technologies or apply advanced configurations.

The combination of AI techniques is vital for observability and security use cases

Generative AI is a transformative technology for delivering productivity gains. Observability, security, and business use cases raise additional challenges as they need precision and reproducibility.

Large language models (LLMs), which are the foundation of generative AIs, are neural networks: they learn, summarize, and generate content based on training data. When a user asks a question, generative AIs create an answer word by word. They predict the probability of the next word or sequence of words given the input prompt. They employ probabilistic sampling techniques and allow controlled randomness to diversify responses.

This means that the same prompt/question will provide different responses. Because this randomized, probabilistic approach is not rooted in precise causal data, a pure generative AI approach renders use cases that require precision impossible.

Davis AI combines AI techniques for precise and reliable outcomes:

• Predictive AI and causal AI provide context to Davis CoPilot. They automatically enrich prompts with specific information, which provides better recommendations and precise, reproducible results.
• Davis CoPilot generative AI doesn’t only react to user inputs. It can also be triggered automatically by predictive AI or causal AI events (for example, to recommend remediation actions automatically).

Hypermodal AI unleashes exponential value: Step-by-step example

In this example, a user builds a Dynatrace dashboard for all business-critical services that will be at risk during Black Friday. The steps required to complete this task can be categorized as either predictive , causal , or generative .

 Understand the meaning of questions.

 Identify all user sessions that contain conversion metrics (using Smartscape).

 Identify all services that are needed for these user journeys (topology using Smartscape).

 Predict how these services will behave under a higher load based on historic data.

 Choose the services that are nearing their limits (topology metrics).

  Choose the services that caused problems in the past.

  Use input to generate a dashboard and queries.

 Determine if remediation workflows should be set up.

Dynatrace Davis^® AI provides answers and automation, and boosts productivity for multifaceted use cases

Automatic root cause analysis

Davis AI automatically detects user-facing issues and assesses their impact on the business and affected users. Then, Davis uses context—such as topology, transaction, and code-level information—to identify the precise root cause of problems.

Davis CoPilot can provide recommended actions to remediate issues.

Automatic root cause analysis enables AIOps (or AI for IT operations) automation using the Dynatrace AutomationEngine.

Davis AI with predictive AI and causal AI is generally available and used by all Dynatrace customers. Start your free trial now! Davis CoPilot™ will be available in 2024 as a core technology within the Dynatrace platform. Find more information and interesting links here.

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