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

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

The problem: Context switching kills developer productivity

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

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

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

The solution: Kiro powers and intelligent observability

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

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

No switching between tools. No waiting on another team.

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

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

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

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

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

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

Beyond root cause: Automation across the entire workflow

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

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

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

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

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

Proactive alerting: Catching problems before they crash

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

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

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

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

Debugging a sneaky production bug

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

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

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

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

Start embedding observability into your development workflow

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

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

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

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

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

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

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

Agentic observability is evolving

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

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

The observability gap in AI agent deployments

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

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

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

New enhancements for teams building with Amazon Bedrock AgentCore

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

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

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

What’s next

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

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

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

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

Get started with Dynatrace AI Observability for Amazon Bedrock AgentCore agents

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

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

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

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

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

Why this collaboration matters

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

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

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

Tackling key customer priorities with Dynatrace and AWS

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

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

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

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

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

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

Driving the future of cloud modernization

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

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

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