Accelerating AKS troubleshooting with the Azure Copilot Observability Agent
AKS incidents rarely stay within one Kubernetes object, signal, or tool. A latency spike might first appear in application telemetry, but the root cause may sit elsewhere: pod restarts, node pressure, scheduling failures, or a recent configuration change. The Azure Copilot Observability Agent in Azure Monitor helps connect these signals into an explainable investigation, so teams can move from symptoms to evidence-backed next steps. Why AKS troubleshooting is complex Troubleshooting Azure Kubernetes Service (AKS) is complex because failures can originate in workloads, platform components, infrastructure, or the application code running on the cluster. For example, pods stuck in Pending may indicate capacity or scheduling issues, while application latency may be caused by throttling, failed probes, pod restarts, or node pressure below the app. During an incident, simply having more telemetry is not enough. Teams need a way to test likely causes, rule out unrelated signals, and keep the investigation tied to the affected workload and time window. From signal to root cause: the investigation flow The Observability Agent follows a consistent investigation pipeline: Scope the problem by identifying the most likely infrastructure resources involved, plus connected dependencies. Collect data across metrics, logs, traces, change history, and related signals. Detect anomalies using learned baselines (for metrics) and log analysis. Correlate across resources spanning infrastructure and application layers. Run deep diagnostics by invoking resource-specific tools when needed to pinpoint root cause. Summarize findings in a structured format: what happened, why it happened, and what to do next. AKS investigation data sources The agent works with telemetry already available in your Azure Monitor environment. Investigation depth improves as more relevant signals are enabled, including Container insights logs, Kubernetes events and state, Azure managed service for Prometheus, container and pod logs, Application Insights telemetry for AKS-hosted workloads, Azure Activity Log changes, control plane logs routed through diagnostic settings, and resource metadata for the cluster, node pools, workloads, and related Azure resources. Figure 1. AKS investigation data sources You don’t need to enable every telemetry source to get started. The Observability Agent uses the data already available in Azure Monitor, and its findings become more complete as more AKS and application signals are collected. Example 1: AKS infrastructure — explaining why new pods never start Consider a workload rollout on AKS where replacement pods remain stuck in Pending state. What looks like a failed release may stem from the workload definition, cluster state, or underlying infrastructure. Investigation walkthrough Symptom: rollout is blocked Replacement pods remain in Pending during rollout, and Kubernetes events show repeated scheduling failures. This indicates that the rollout is blocked before new pods can start. Workload evidence: scheduling, not startup Pod state identifies the affected workload, while Kubernetes events show repeated placement failures. The issue is therefore tied to scheduling rather than application startup or container crash behavior. Cluster evidence: capacity pressure When enabled, Prometheus node metrics show CPU and memory utilization near capacity. Cluster-level trends show resource pressure increasing at the same time as pending pods and scheduling failures. Likely cause: insufficient schedulable capacity The scheduler cannot place new pods because the relevant node pool does not have enough available capacity. The failed rollout is best explained by capacity pressure in the target node pool rather than an application crash or image startup failure. Recommended action Scale out the affected node pool or adjust workload resource requests, then retry the rollout once schedulable capacity is restored. Figure 2. AKS investigation flow The Observability Agent connects pod state, scheduling events, and node pressure to explain why the rollout is blocked and which capacity action to consider next. Example 2: Joint app-AKS investigation — tracing application latency to pod restarts Now consider a customer-facing application where users see increased latency and intermittent HTTP 5xx errors after deployment. The first symptom appears in application telemetry, but the unhealthy requests are served by pods that are repeatedly restarting in AKS. Investigation walkthrough Symptom: customer-facing service degradation After deployment, application telemetry shows increased latency and HTTP 5xx errors. The first visible impact appears at the application layer. AKS evidence: unstable pods Affected pods enter CrashLoopBackOff, restart counts increase, and Kubernetes events show back-off restarts, probe failures, or image or command errors. Container logs point to startup exceptions, missing configuration, or crash details. Resource evidence: workload-specific pressure Container memory usage approaches configured limits before restarts, while node metrics show no broad node pressure. This suggests the issue is workload-specific rather than cluster-wide capacity related. Change evidence: deployment correlation Deployment history shows a new image or configuration change shortly before restarts began, with no matching platform health event. The timing points to the latest deployment or configuration change. Recommended action Review the latest image or configuration change, inspect container logs, adjust memory limits, or roll back if needed. Focus remediation on the workload change rather than node pool scaling. This pattern shows how an application symptom can map back to AKS workload behavior. Application telemetry establishes the user impact, while Kubernetes events, container logs, and resource metrics help explain why the affected pods keep failing. Operational impact For site reliability engineers, platform teams, and IT professionals, the Observability Agent reduces the time spent moving between application and AKS telemetry. It brings relevant signals into one investigation, surfaces supporting evidence, and applies Azure Monitor and AKS context so your team can review the findings, validate the recommended path, and decide which production changes to make. Figure 3. AKS investigation results Using the Observability Agent You can start using the Observability Agent from the Azure portal in two common AKS troubleshooting flows: Investigation mode: Start an investigation from an Azure Monitor alert on an AKS resource or from an Application Insights alert for an AKS-hosted workload. The agent uses the alert context to scope the incident, correlate application and cluster telemetry, and summarize the likely cause with recommended next steps. Chat-based exploration: Open the Monitor experience in AKS and select the Observability Agent button to chat with your telemetry. Use natural language to ask follow-up questions, explore logs and metrics, detect and inspect anomalies, and narrow down likely causes. Figure 4. Starting Observability Agent from AKS Monitor experience Next steps Azure Copilot Observability Agent overview Monitor Azure Kubernetes Service with Azure Monitor Stay connected Follow this blog for ongoing deep dives, updates on current capabilities, and a preview of what's coming next. Live webinar — A walkthrough of real Observability Agent scenarios, best practices, and what's available today, along with a look at what's coming next and live Q&A with the product team. Register for the Observability Agent webinar. We'd love your feedback The Observability Agent continues to evolve based on real-world usage and operator feedback. Share your thoughts directly through the Give Feedback option in the experience, or reach us at: azureobsagent@microsoft.comInside the Observability Agent: How Deep Investigations and Reasoning Work
Deep investigation in the Azure Copilot Observability Agent turns observability data into a verified, data-backed explanation of what happened during an incident, correlating application, infrastructure, and platform signals across time, scope, and type to produce a structured root cause analysis.
Azure Monitor Copilot Observability Agent: What’s new at Build
The Observability agent in Azure Copilot is an AI-powered assistant built into Azure Monitor that helps engineers investigate issues and explore their systems using natural language. By grounding its analysis in telemetry data such as metrics, logs, and traces, it supports both open-ended exploration and guided troubleshooting. For more details, see the documentation. Since our initial public preview, the Observability agent in Azure Copilot has continued to evolve with new capabilities and expanded coverage (You can read more about the initial release in our previous blog) At Build 2026, we’re introducing updates that expand the Observability agent’s capabilities and the range of scenarios it can support. These updates provide deeper analysis and more detailed responses for both exploration and investigation. Expanded Investigation Scenarios The Observability agent now supports a broader set of scenarios across applications and infrastructure. These can be accessed directly from relevant product experiences, without requiring a prior alert, allowing teams to explore data conversationally and initiate deeper investigations as signals emerge. Integration with Microsoft Foundry AI Agent The Observability agent integrates with Microsoft Foundry AI Agents, enabling correlation of signals across key generative AI and agent observability scenarios such as latency spikes, error patterns, and tool invocation failures. Teams can interact with the Observability agent either from alerts - including alerts based on Foundry telemetry - or directly within Application Insights, where the Agents details experience serves as the primary entry point. From there, users can use the Observability agent to diagnose errors, analyze trends, and explore their data across one or multiple agents. Application Insights integration The Observability agent enables investigation of failure scenarios directly from Application Insights Failures blade, allowing teams to analyze application-level issues and move from symptom to root cause. Azure Kubernetes Service (AKS) integration The Observability agent enables deep investigation of issues in Azure Kubernetes Service (AKS) clusters. AKS investigations correlate signals from Azure Monitor with Kubernetes logs and events, and (coming soon) Prometheus metrics stored in an Azure Monitor Workspace. Together, these signals enable full‑stack analysis of applications running on AKS. The Observability agent helps teams determine whether an issue originates from the application or from the underlying Kubernetes platform, reducing time to diagnosis and resolution. Activity Logs integration Investigations can be initiated based on Azure Resource Health events surfaced in Activity Logs, enabling analysis of service-impacting signals related to the Azure platform. Deeper Insights across systems Multiple Application Insights - Coming soon! The Observability agent supports investigations that can span multiple Application Insights resources, enabling scenarios that involve multiple services within distributed applications. The agent can guide users to expand the investigation scope when cross-service issues are detected. Integration with Azure Service Health The Observability agent correlates investigation context with Azure Service Health events, helping teams understand potential platform impact as part of their investigation. This helps distinguish application-level issues from broader Azure platform conditions and prioritize active impacts. Issue management Enhancements Viewing issues Issues can now be viewed in multiple places, depending on the required scope: Azure Monitor: showing issues across all Azure Monitor Workspaces (AMWs) under the selected subscriptions Azure Monitor Workspace: showing issues stored within a specific AMW Issue actions & notifications Issue actions trigger notifications when issues are created or updated, enabling integration with workflows such as email, webhooks, and automation. Sharing and follow-up You can now download investigation results as a PDF, including supported data, enabling teams to capture and share investigation context for incident reviews and reporting. Coming Soon Billing for the Observability agent starts on July 1, 2026. The agent uses a consumption-based pricing model, so customers pay only for the AI work the agent performs. Agent consumption is measured in Azure Agent Credit (AAC) units, which reflect how many LLM tokens the agent used. For more details, see the documentation. Stay connected Follow this blog for ongoing updates and deeper dives into new capabilities Join our upcoming webinar for real-world scenarios, best practices, and a look at what’s coming next 👉 Register here We’d love your feedback The Observability Agent continues to evolve based on real-world usage and customer feedback. Share feedback through the Give Feedback option in the product or contact us at: azureobsagent@microsoft.com Want to learn more? Read our previous blog posts - Public Preview Update: Azure Copilot Observability Agent | Microsoft Community Hub The Azure Copilot Observability Agent Chat - Stop Writing Queries, Start Asking Questions. | Microsoft Community Hub Explore our documentation - Azure Copilot observability agent (preview) - Azure Monitor | Microsoft Learn
Connect Metrics to Traces with Exemplars in Azure Monitor
Following Microsoft’s recent GA announcement for OpenTelemetry (OTel) support, we are excited to announce support for Exemplars for customers instrumenting metrics with Prometheus or OpenTelemetry and traces using OpenTelemetry, enhancing Azure Monitor’s integrated observability experience for cloud-native applications. Modern cloud-native applications generate enormous volumes of telemetry. Metrics help teams detect that something is wrong, but traces explain why. Exemplars bridge these two worlds by attaching trace references directly to metric data points, making it dramatically easier to pivot from a spike in latency or errors to the exact distributed trace responsible for the issue. With Azure Monitor, customers can now ingest metrics with exemplars and visualize them in Azure Managed Grafana. This enables seamless correlation between metrics and traces, helping engineering teams troubleshoot issues faster and reduce mean time to resolution (MTTR). Why Exemplars Matter Traditional monitoring workflows often require users to manually correlate data across multiple systems. Exemplars simplify this workflow by embedding trace context directly into metric samples. For example, if a latency metric spikes at a specific timestamp, the exemplar associated with that data point can link directly to the distributed trace responsible for the outlier. This provides several benefits: Faster root cause analysis Quicker transition from aggregate metrics to request-level details Simplified debugging workflows for SRE and platform teams Better observability experiences for microservices and distributed applications Unified Observability with Azure Monitor With Azure Monitor and Azure Managed Grafana, you can now: Ingest OTLP or Prometheus metrics with exemplars into Azure Monitor Workspace Store and analyze traces in Azure Monitor Application Insights Visualize exemplar markers directly in Grafana charts Navigate from a metric spike to the exact distributed trace associated with that data point By combining these signals in a single observability platform, organizations can correlate infrastructure health, application behavior, and request traces without context switching between tooling. How It Works Once metrics, exemplars, and traces are ingested into Azure Monitor, Azure Managed Grafana can consume exemplar information from the configured Prometheus data source. When exemplars are enabled in Grafana dashboards, users will see markers associated with individual metric data points. Selecting an exemplar opens the associated trace in Azure Monitor, providing end-to-end diagnostic context. Getting Started Setup data ingestion: Instrument your application to emit OpenTelemetry traces, OpenTelemetry or Prometheus metrics with exemplars, and enable ingestion of the same to Azure Monitor using OpenTelemetry Collector. Follow the instructions in Ingest OTLP Data into Azure Monitor with OTel Collector - Azure Monitor | Microsoft Learn. After this step, you will have the Log Analytics Workspace, Azure Monitor Workspace and Application Insights resources all set up to store the telemetry data. Create an Azure Managed Grafana instance and connect it with the Azure Monitor Workspace by navigating to your Azure Monitor Workspace in the Azure portal and then clicking on “Linked Grafana workspaces”. To learn more, see Manage an Azure Monitor workspace - Azure Monitor | Microsoft Learn Optionally, enable Azure Managed Prometheus on your AKS cluster or use remote-write and configure it to use the same Azure Monitor Workspace to centralize infrastructure and application metrics. Enable Exemplars in Azure Managed Grafana: After setting up the data ingestion, ensure that logs and traces are flowing into Log Analytics Workspace, and metrics are flowing into Azure Monitor Workspace. Step 1: Enable Exemplars on Prometheus Data Source in Azure Managed Grafana Navigate to Connections -> Data Sources in Azure Managed Grafana. Since you have connected Azure Managed Grafana to Azure Monitor Workspace, you will see the data source (Managed_Prometheus_<AMW-Name>) already configured. If the data source is not configured, follow the steps here to add your Azure Monitor Workspace as a data source. Open the data source configuration. Click Add Exemplars to enable exemplar support. Step 2: Configure Trace Linking with Azure Monitor In the exemplar configuration section, toggle Internal Link to On. Select Azure Monitor as the data source. In the Label Name, enter the name of the field in the labels object that should be used to get the trace id, eg. trace_id. Click Save & Test. This configuration enables direct navigation from exemplar markers in Grafana charts to the associated traces stored in Azure Monitor. Azure Managed Grafana also supports trace correlation from other solutions like Jaeger etc. To use your trace solution, use the appropriate links. Step 3: Enable Exemplars in Dashboards Navigate to a Grafana dashboard that uses your configured Prometheus data source. Open the panel options for a metrics chart. Toggle Exemplars to On. Once enabled, exemplar markers will appear on supported metric visualizations. Clicking on it will show exemplar details along with an option to open the corresponding distributed trace in Azure Monitor. To learn more, visit https://aka.ms/azmon-exemplarsNew Capabilities to Observe Agents in Azure Monitor
Over the last six months, we have been listening to you and building new capabilities to help you observe your agents. You’ve been sharing with us that quality issues are tricky and evaluation is critical, that agent reasoning needs to be understood, that humans must be in the loop to review select agent interactions, and that security and privacy are essential. To address these concerns, we’re announcing several new capabilities that make agents a first-class artifact in Azure Monitor, so you can debug them in the context of your broader distributed application alongside non-agentic components. Microsoft Foundry remains the surface for building and evaluating agents within the context of your project, while Azure Monitor provides the full-stack observability platform and underlying data foundation that powers those experiences. Today, we’re announcing new capabilities in Azure Monitor across ingestion, performance, evaluation workflows, agent debugging, and instrumentation updates to help teams get telemetry faster, inspect agent behavior more deeply, and standardize observability across hosting environments and frameworks. What’s new Reducing pipeline latency from more than 60 seconds to 7.5 seconds at P90. This makes telemetry available faster for teams troubleshooting agents at scale. Emitting events up to 1MB and up to 256kB per attribute. Prompts and responses can get large, and this helps avoid data truncation. Introducing a new view that shows a list of all agents being monitored. Whether you use Microsoft Agent Framework, LangChain, Microsoft Copilot Studio, Foundry Hosting, AKS Hosting, or something else, they all show up here. Improving drill-in from Evaluations to underlying prompts/responses. Evaluations in Azure Monitor are powered by Foundry, and we continue to improve visuals. Showing conversation context in end-to-end transaction view. In chat agents, conversations have become critical glue that connects traces and eases debugging. Searching by text and showing prompt previews in end-to-end transaction view. Prompts and responses are essential to understanding agent logic, and now you can search based on keyword text in Search and End-to-end transaction details views. Show evaluation scores in end-to-end transaction details and sort by evaluation score in Search. Evaluation is emerging as a “4 th pillar” of telemetry, and you’ll see it surface more prominently across Azure Monitor Application Insights. Access the entire JSON blob of prompt/response text. This makes it easier to get to your underlying data and copy out of Azure Monitor for custom analysis/evaluation. Adding a “trace tree” to enhance traversing the agent’s reasoning logic. This new addition to end-to-end transaction view makes traversing long-traces much easier. Enabling builders to annotate (i.e., manual evaluations) from transaction details. Get rid of spreadsheets on the side and annotate from within Azure Monitor. Enabling capture of end-user feedback (i.e., thumbs up/down). Brings end-user feedback alongside other telemetry for more powerful troubleshooting. Extending AI-powered troubleshooting to agents. Observability agent offers full-stack, AI-powered troubleshooting and surfaces up findings in an issue. Learn More. Observability of Coding Agents. Get end-to-end visibility into agent and model usage, performance, and cost with Azure Monitor Application Insights, and built-in Grafana dashboards. Learn More. A unified “Microsoft OpenTelemetry Distro” to observe agents hosted anywhere. A unified Microsoft OpenTelemetry Distro for observing agents hosted anywhere gives teams a single starting point across Foundry, Azure Monitor, and A365, reducing fragmentation and simplifying onboarding (GH Repos: Python, .NET, JavaScript). Skills-based enablement. Getting started is easier. Just point your agent to a skill for AI-assisted instrumentation. We also plan to upgrade tools for instrumentation in Azure MCP. What’s next We’re continuing to invest in this area, with upcoming work focused on stronger security controls for prompts and responses, better cost transparency for agents, and clearer ways to measure ROI across your agent fleet. These updates make it possible to observe agents without adopting a separate toolchain. Explore the new capabilities, and if you see gaps, let us know so we can continue shaping the roadmap based on your feedback. Learn More.What’s new in Observability at Build 2026
At Build 2026, Azure Monitor introduces major advancements in end-to-end observability, extending across AI agents, applications, and infrastructure with OpenTelemetry at its core. New capabilities with Azure Copilot Observability agent, SLI/SLO support, and smarter alerting help teams move faster from detection to root cause while reducing noise and manual effort. Together, these innovations enable developers and SREs to operate modern, AI-driven systems with greater insight, efficiency, and alignment to customer experience.Monitor AI coding agents with OpenTelemetry in Azure Monitor
AI coding agents are quickly becoming part of the everyday developer workflow. As teams adopt tools such as GitHub Copilot, Claude Code, and Codex, they need a better way to understand usage, troubleshoot performance, and keep an eye on token consumption and cost. With Azure Monitor’s OpenTelemetry support, you can collect OpenTelemetry Protocol (OTLP) signals from AI coding agents and route them into Azure Monitor for end-to-end visibility. Ingested OTLP data is stored with OpenTelemetry semantics for logs and traces, Application Insights provides curated agent views for troubleshooting, detailed trace visualizations and end-to-end transaction views. Image: Application Insights end-to-end transaction view for agents. Azure Monitor also includes ready-to-use Grafana dashboards that deliver streamlined, out-of-the-box visualizations with the flexibility to customize further. This gives platform teams, engineering leaders, and developers a consistent way to monitor using open-source standards. The key takeaway is that these dedicated coding agent dashboards surface agent-specific details like feature usage, commit counts, code change acceptance rates, and user details if included in ingested telemetry. That creates immediate value for developer teams and organizations that want to understand adoption rates and the value being returned by coding agents. Image: Azure Monitor dashboards with Grafana for GitHub Copilot How it works Coding agents or IDEs can be configured to export OTLP signals by using organization-wide environment variables, project settings, or shared repository configurations. Note: These settings determine whether content and conversation details are captured and exported. Ensure that your configuration matches your organization's privacy and data handling policies. An OpenTelemetry Collector can receive OTLP and forward it to Azure Monitor OTLP ingestion endpoints. This OTLP ingestion pipeline uses Entra authenticated and stores logs and traces with OpenTelemetry semantics Once the data is in Azure Monitor, teams can investigate usage and adoption patterns in Application Insights agent-specific views and visualize trends with pre-built coding agent dashboards in Azure Monitor dashboards with Grafana or Azure Managed Grafana. Image: OTLP ingestion path from coding agent to Azure Monitor Why it matters This approach helps central IT and engineering management teams understand rollout, adoption, and cost across their organization, while giving developers a better view of agent interactions and productivity signals. With OpenTelemetry and Azure Monitor, teams can standardize once, reduce pipeline complexity, and access useful insights faster for these coding agents: GitHub Copilot Claude Code Codex OpenClaw Gemini CLI OpenCode Get started AI coding agents are accelerating software development, and observability needs to keep up. Azure Monitor brings together OpenTelemetry and Grafana so you can monitor agent usage and performance with a flexible, standards-based approach. To learn more, explore: OpenTelemetry export from Visual Studio Code OTLP ingestion into Azure Monitor Coding agent dashboards in Azure Managed Grafana Monitor AI agents with Application InsightsDirect OpenTelemetry ingestion into Azure Monitor is now generally available
OpenTelemetry is powering a new era of observability. Built on open standards, designed for portability, and made for developers who want flexibility without compromise. And now, you can send OpenTelemetry logs, metrics, and traces straight into Azure Monitor OTLP endpoints and data storage. This capability is generally available, production-ready, and built to scale from day one. With direct OTLP ingestion, you can keep your existing OpenTelemetry instrumentation and OpenTelemetry collector pipelines while sending telemetry to Azure Monitor for investigation in Application Insights, analysis in Log Analytics, Prometheus metric storage and visualization in Grafana. What’s now generally available Direct OTLP ingestion into Azure Monitor for logs, metrics, and traces. Production-ready onboarding for deploying data collection rules and endpoints. Application Insights experiences for distributed tracing, performance investigation, and troubleshooting powered by OTLP data. Grafana dashboards ready-to-use for visualizing OpenTelemetry signals. Prometheus data storage and query language for metrics OpenTelemetry semantic conventions for logs and traces, so data lands in a familiar standards-based schema. How to send OTLP to Azure Monitor Instrument your application with OpenTelemetry using the open-source SDKs and configure OTLP export to an OpenTelemetry Collector. Configure Azure Monitor OTLP ingestion by using an Application Insights resource with OTLP support, which sets up the required Azure Monitor resources and investigation experiences or manually create the required resources. Export traces, metrics, and logs directly to Azure Monitor from the OpenTelemetry Collector using the built-in OTLP over HTTP exporter. Get started Where your telemetry lands Azure Monitor brings these signals together so your teams can triage and troubleshoot root cause faster without modifying code and instrumentation. Metrics are stored in an Azure Monitor Workspace, a Prometheus metrics store. Logs and traces are stored in a Log Analytics workspace using an OpenTelemetry semantic conventions–based schema. Application Insights lights up distributed tracing and end-to-end performance investigations. Pre-built Grafana dashboards for OpenTelemetry metrics are available directly in the Azure portal alongside Application Insights. Why it matters Standardize once: Instrument with OpenTelemetry and keep your instrumentation vendor neutral and keep your telemetry portable. Reduce overhead: Fewer bespoke exporters and pipelines to maintain. Stick to OTLP for all cases. Debug faster: Correlate metrics, logs, and traces to get from reported issues to root cause with less guesswork. Observe with confidence: Use dashboards and tracing views that are ready on day one. Next step: Try OTLP export from your environment to Azure Monitor, then validate end-to-end signal flow with Application Insights and Grafana dashboards. Get startedTroubleshoot with OpenTelemetry in Azure Monitor - Public Preview
OpenTelemetry is fast becoming the industry standard for modern telemetry collection and ingestion pipelines. With Azure Monitor’s new OpenTelemetry Protocol (OTLP) support, you can ship logs, metrics, and traces from wherever you run workloads to analyze and act on your observability data in one place. What’s in the preview Direct OTLP ingestion into Azure Monitor for logs, metrics, and traces. Automated onboarding for AKS workloads. Application Insights on OTLP for distributed tracing, performance and troubleshooting experiences. Pre-built Grafana dashboards to visualize signals quickly. Prometheus for metric storage and query. OpenTelemetry semantic conventions for logs and traces, so your data lands in a familiar standard-based schema. How to send OTLP to Azure Monitor: pick your path AKS: Auto-instrument Java and Node.js workloads using the Azure Monitor OpenTelemetry distro, or auto-configure any OpenTelemetry SDK-instrumented workload to export OTLP to Azure Monitor. Get started Limited preview: Auto-instrumentation for .NET and Python is also available. Get started VMs/VM Scale Sets (and Azure Arc-enabled compute): Use the Azure Monitor Agent (AMA) to receive OTLP from your apps and export it to Azure Monitor. Get started Any environment: Use the OpenTelemetry Collector to receive OTLP signals and export directly to Azure Monitor cloud ingestion endpoints. Get started Under the hood: where your telemetry lands Metrics: Stored in an Azure Monitor Workspace, a Prometheus metrics store. Logs + traces: Stored in a Log Analytics workspace using an OpenTelemetry semantic conventions–based schema. Troubleshooting: Application Insights lights up distributed tracing and end-to-end performance investigations, backed by Azure Monitor. Why it matters Standardize once: Instrument with OpenTelemetry and keep your telemetry portable. Reduce overhead: Fewer bespoke exporters and pipelines to maintain. Debug faster: Correlate metrics, logs, and traces to get from alert to root cause with less guesswork. Observe with confidence: Use dashboards and tracing views that are ready on day one. Next step: Try the OTLP preview in your environment, then validate end-to-end signal flow with Application Insights and Grafana dashboards. Learn More
