{"id":45805,"date":"2025-08-27T08:00:00","date_gmt":"2025-08-27T15:00:00","guid":{"rendered":""},"modified":"2025-09-15T10:33:22","modified_gmt":"2025-09-15T17:33:22","slug":"agent-factory-top-5-agent-observability-best-practices-for-reliable-ai","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/agent-factory-top-5-agent-observability-best-practices-for-reliable-ai\/","title":{"rendered":"Agent Factory: Top 5 agent observability best practices for reliable AI"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>This blog post is the third out of a six-part blog series called&nbsp;<\/em><a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/tag\/agent-factory\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Agent Factory<\/em><\/a><em>&nbsp;which will share best practices, design patterns, and tools to help guide you through adopting and building agentic AI.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"seeing-is-knowing-the-power-of-agent-observability\">Seeing is knowing\u2014the power of agent observability<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">As agentic AI becomes more central to enterprise workflows, ensuring reliability, safety, and performance is critical. That\u2019s where agent observability comes in. Agent observability empowers teams to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\">Detect and resolve issues early in development.<\/li>\n\n\n\n<li class=\"wp-block-list-item\">Verify that agents uphold standards of quality, safety, and compliance.<\/li>\n\n\n\n<li class=\"wp-block-list-item\">Optimize performance and user experience in production.<\/li>\n\n\n\n<li class=\"wp-block-list-item\">Maintain trust and accountability in AI systems.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">With the rise of complex, multi-agent and multi-modal systems, observability is essential for delivering AI that is not only effective, but also transparent, safe, and aligned with organizational values. Observability empowers teams to build with confidence and scale responsibly by providing visibility into how agents behave, make decisions, and respond to real-world scenarios across their lifecycle.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/azure.microsoft.com\/en-us\/products\/ai-foundry\">Learn more about building agentic AI in Azure AI Foundry<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-is-agent-observability\">What is agent observability?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Agent observability is the practice of achieving deep, actionable visibility into the internal workings, decisions, and outcomes of AI agents throughout their lifecycle\u2014from development and testing to deployment and ongoing operation. Key aspects of agent observability include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\"><strong>Continuous monitoring:<\/strong>&nbsp;Tracking agent actions, decisions, and interactions in real time to surface anomalies, unexpected behaviors, or performance drift.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Tracing:<\/strong>&nbsp;Capturing detailed execution flows, including how agents reason through tasks, select tools, and collaborate with other agents or services. This helps answer not just \u201cwhat happened,\u201d but \u201cwhy and how did it happen?\u201d<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Logging:<\/strong> Records agent decisions, tool calls, and internal state changes to support debugging and behavior analysis in agentic AI workflows.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Evaluation:<\/strong>&nbsp;Systematically assessing agent outputs for quality, safety, compliance, and alignment with user intent\u2014using both automated and human-in-the-loop methods.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Governance:<\/strong>&nbsp;Enforcing policies and standards to ensure agents operate ethically, safely, and in accordance with organizational and regulatory requirements.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"traditional-observability-vs-agent-observability\">Traditional observability vs agent observability<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Traditional observability relies on three foundational pillars:&nbsp;metrics,&nbsp;logs, and&nbsp;traces. These provide visibility into system performance, help diagnose failures, and support root-cause analysis. They are well-suited for conventional software systems where the focus is on infrastructure health, latency, and throughput.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, AI agents are non-deterministic and introduce new dimensions\u2014autonomy, reasoning, and dynamic decision making\u2014that require a more advanced observability framework.&nbsp;Agent observability&nbsp;builds on traditional methods and adds two critical components:&nbsp;evaluations&nbsp;and&nbsp;governance. Evaluations help teams assess how well agents resolve user intent, adhere to tasks, and use tools effectively. Agent governance can ensure agents operate safely, ethically, and in compliance with organizational standards.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This expanded approach enables deeper visibility into agent behavior\u2014not just what agents do, but why and how they do it. It supports continuous monitoring across the agent lifecycle, from development to production, and is essential for building trustworthy, high-performing AI systems at scale.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"azure-ai-foundry-observability-provides-end-to-end-agent-observability\">Azure AI Foundry Observability provides end-to-end agent observability<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/observability\" target=\"_blank\" rel=\"noreferrer noopener\">Azure AI Foundry Observability<\/a> is a unified solution for&nbsp;evaluating, monitoring, tracing, and governing&nbsp;the quality, performance, and safety of your AI systems end to end in <a href=\"https:\/\/azure.microsoft.com\/en-us\/products\/ai-foundry\">Azure AI Foundry<\/a>\u2014all built into your AI development&nbsp;loop. From model selection to real-time debugging, Foundry Observability capabilities empower teams to ship production-grade AI with confidence and speed. It&#8217;s observability, reimagined for the enterprise AI era.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With built-in capabilities like the Agents Playground evaluations, Azure AI Red Teaming Agent, and Azure Monitor integration, Foundry Observability brings evaluation and safety into every step of the agent lifecycle. Teams can trace each agent flow with full execution context, simulate adversarial scenarios, and monitor live traffic with customizable dashboards. Seamless CI\/CD integration enables continuous evaluation on every commit and governance support with Microsoft Purview, Credo AI, and Saidot integration helps enable alignment with regulatory frameworks like the EU AI Act\u2014making it easier to build responsible, production-grade AI at scale.<\/p>\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-1024x485.webp\" alt=\"Azure AI Foundry Observability banner showing tabs for Leaderboards, Traces, Logs, Evaluations, Metrics, and Governance, with a lifecycle arrow indicating coverage across the agent and AI development lifecycle. \" class=\"wp-image-45844 webp-format\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-1024x485.webp 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-300x142.webp 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-768x364.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-1536x728.webp 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-2048x971.webp 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Foundry-Observability-1024x485.webp\"><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"five-best-practices-for-agent-observability\">Five best practices for agent observability<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1-pick-the-right-model-using-benchmark-driven-leaderboards\">1. Pick the right model using benchmark driven leaderboards<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Every agent needs a model and choosing the right model is foundational for agent success. While planning your AI agent, you need to decide which model would be the best for your use case in terms of safety, quality, and cost.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You can pick the best model by either evaluating the model on your own data or use Azure AI Foundry\u2019s&nbsp;<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/how-to\/benchmark-model-in-catalog\" target=\"_blank\" rel=\"noreferrer noopener\">model leaderboards<\/a>&nbsp;to compare foundation models out-of-the-box by quality, cost, and performance\u2014backed by industry benchmarks. With Foundry model leaderboards, you can find model leaders in various selection criteria and scenarios, visualize trade-offs among the criteria (e.g., quality vs cost or safety), and dive into detailed metrics to make confident, data-driven decisions.<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-1024x442.webp\" alt=\"Screenshot of Azure AI Foundry model leaderboard dashboard, displaying comparative bar charts for model quality, safety, cost, and throughput, and detailed evaluation metrics for different AI models.\" class=\"wp-image-45845 webp-format\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-1024x442.webp 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-300x130.webp 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-768x332.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-1536x664.webp 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-2048x885.webp 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/model-leaderboards-1024x442.webp\"><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>Azure AI Foundry\u2019s model leaderboards gave us the confidence to scale client solutions from experimentation to deployment. Comparing models side by side helped customers select the best fit\u2014balancing performance, safety, and cost with confidence.<\/em><\/p>\n<cite>\u2014Mark Luquire, EY Global Microsoft Alliance Co-Innovation Leader, Managing Director, Ernst &amp; Young, LLP*<\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2-evaluate-agents-continuously-in-development-and-production\">2. Evaluate agents continuously in development and production<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Agents are powerful productivity assistants. They can plan, make decisions, and execute actions. Agents typically first&nbsp;<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/agent-evaluators#intent-resolution\" target=\"_blank\" rel=\"noreferrer noopener\">reason through user intents in conversations<\/a>,&nbsp;<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/agent-evaluators#tool-call-accuracy\" target=\"_blank\" rel=\"noreferrer noopener\">select the correct tools<\/a>&nbsp;to call and satisfy the user requests, and&nbsp;<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/agent-evaluators#task-adherence\" target=\"_blank\" rel=\"noreferrer noopener\">complete various tasks<\/a>&nbsp;according to their instructions.&nbsp;Before deploying agents, it\u2019s critical to evaluate their behavior and performance.<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-1024x565.webp\" alt=\"Alt-text: Diagram illustrating agent evaluation steps: intent resolution, tool calling, and response assembly, with example user query and evaluation criteria for each step. \" class=\"wp-image-45846 webp-format\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-1024x565.webp 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-300x165.webp 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-768x424.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-1536x847.webp 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents.webp 1742w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/evaluation-agents-1024x565.webp\"><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Azure AI Foundry makes agent evaluation easier with several agent evaluators supported out-of-the-box, including Intent Resolution (how accurately the agent identifies and addresses user intentions), Task Adherence (how well the agent follows through on identified tasks), Tool Call Accuracy (how effectively the agent selects and uses tools), and Response Completeness (whether the agent\u2019s response includes all necessary information). Beyond agent evaluators, Azure AI Foundry also provides a comprehensive suite of evaluators for broader assessments of AI quality, risk, and safety. These include quality dimensions such as\u00a0<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/rag-evaluators#relevance\" target=\"_blank\" rel=\"noreferrer noopener\">relevance<\/a>,\u00a0<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/general-purpose-evaluators#coherence\" target=\"_blank\" rel=\"noreferrer noopener\">coherence<\/a>, and\u00a0<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/general-purpose-evaluators#fluency\" target=\"_blank\" rel=\"noreferrer noopener\">fluency<\/a>,\u00a0along with comprehensive\u00a0<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/evaluation-evaluators\/risk-safety-evaluators\" target=\"_blank\" rel=\"noreferrer noopener\">risk and\u00a0safety checks<\/a>\u00a0that assess for code vulnerabilities, violence,\u00a0self-harm,\u00a0sexual\u00a0content,\u00a0hate, unfairness,\u00a0indirect\u00a0attacks, and\u00a0the use of\u00a0protected\u00a0materials. The Azure AI Foundry Agents Playground brings these evaluation and tracing tools together in one place, letting you test, debug, and improve agentic AI efficiently.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>The robust evaluation tools in Azure AI Foundry help our developers continuously assess the performance and accuracy of our AI models, including meeting standards for coherence, fluency, and groundedness.<\/em><\/p>\n<cite>\u2014<a href=\"https:\/\/www.microsoft.com\/en\/customers\/story\/24300-hughes-azure-ai-foundry?msockid=3e18774cbd40649e0ed362b3bc0c65eb\" target=\"_blank\" rel=\"noreferrer noopener\">Amarender Singh, Director, AI, Hughes Network Systems<\/a><\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3-integrate-evaluations-into-your-ci-cd-pipelines\">3. Integrate evaluations into your CI\/CD pipelines<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Automated evaluations should be part of your CI\/CD pipeline so every code change is tested for quality and safety before release. This approach helps teams catch regressions early and can help ensure agents remain reliable as they evolve.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Azure AI Foundry integrates with your CI\/CD workflows using <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/how-to\/evaluation-github-action?tabs=foundry-project\" target=\"_blank\" rel=\"noreferrer noopener\">GitHub Actions<\/a> and <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/how-to\/evaluation-azure-devops?tabs=foundry-project\" target=\"_blank\" rel=\"noreferrer noopener\">Azure DevOps extensions<\/a>, enabling you to auto-evaluate agents on every commit, compare versions using built-in quality, performance, and safety metrics, and leverage confidence intervals and significance tests to support decisions\u2014helping to ensure that each iteration of your agent is production ready.<\/p>\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-733x1024.webp\" alt=\"Screenshot of Azure AI Evaluation dashboard comparing operational and AI quality metrics across different agent variants, including intent resolution, task adherence, and risk\/safety scores. \" class=\"wp-image-45847 webp-format\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-733x1024.webp 733w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-215x300.webp 215w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-768x1073.webp 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-1099x1536.webp 1099w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result.webp 1254w\" sizes=\"(max-width: 733px) 100vw, 733px\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/github-action-multi-agent-result-733x1024.webp\"><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>We\u2019ve integrated Azure AI Foundry evaluations directly into our GitHub Actions workflow, so every code change to our AI agents is automatically tested before deployment. This setup helps us quickly catch regressions and maintain high quality as we iterate on our models and features.<\/em><\/p>\n<cite>\u2014Justin Layne Hofer, Senior Software Engineer, Veeam<\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"4-scan-for-vulnerabilities-with-ai-red-teaming-before-production\">4. Scan for vulnerabilities with AI red teaming before production<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Security and safety are non-negotiable. Before deployment, proactively test agents for security and safety risks by simulating adversarial attacks. Red teaming helps uncover vulnerabilities that could be exploited in real-world scenarios, strengthening agent robustness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/ai-red-teaming-agent\" target=\"_blank\" rel=\"noreferrer noopener\">Azure AI Foundry\u2019s AI Red Teaming Agent<\/a> automates adversarial testing, measuring risk and generating readiness reports. It enables teams to simulate attacks and validate both individual agent responses and complex workflows for production readiness.<\/p>\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/metric-dashboard-red-team-1-1024x187-1.webp\" alt=\"Metric dashboard showing attack risk categories and percentages for successful attacks, hate and unfairness, self-harm, sexual, and violence, used for AI red teaming evaluation. \" class=\"wp-image-45848 webp-format\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/metric-dashboard-red-team-1-1024x187-1.webp\"><\/figure>\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/detailed-metrics-results-1-1024x526-1.webp\" alt=\"Detailed metrics result table listing attack success, risk category, attack technique, complexity, and human feedback for various adversarial test cases in AI red teaming. \" class=\"wp-image-45849 webp-format\" data-orig-src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/detailed-metrics-results-1-1024x526-1.webp\"><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>Accenture is already testing the&nbsp;<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/ai-red-teaming-agent\" target=\"_blank\" rel=\"noreferrer noopener\">Microsoft AI Red Teaming Agent<\/a>,&nbsp;which simulates adversarial prompts and detects model and application risk posture proactively. This tool will help validate not only individual agent responses, but also full multi-agent workflows in which cascading logic might produce unintended behavior from a single adversarial user. Red teaming lets us simulate worst-case scenarios before they ever hit production. That changes the game.<\/em><\/p>\n<cite>\u2014<a href=\"https:\/\/www.microsoft.com\/en\/customers\/story\/23953-accenture-azure-ai-foundry\" target=\"_blank\" rel=\"noreferrer noopener\">Nayanjyoti Paul, Associate Director and Chief Azure Architect for Gen AI, Accenture<\/a><\/cite><\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"5-monitor-agents-in-production-with-tracing-evaluations-and-alerts\">5. Monitor agents in production with tracing, evaluations, and alerts<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Continuous monitoring after deployment is essential to catch issues, performance drift, or regressions in real time. Using evaluations, tracing, and alerts helps maintain agent reliability and compliance throughout its lifecycle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Azure AI Foundry observability enables continuous agentic AI monitoring through a unified dashboard powered by Azure Monitor Application Insights and Azure Workbooks. This dashboard provides real-time visibility into performance, quality, safety, and resource usage, allowing you to run continuous evaluations on live traffic, set alerts to detect drift or regressions, and trace every evaluation result for full-stack observability. With seamless navigation to Azure Monitor, you can customize dashboards, set up advanced diagnostics, and respond swiftly to incidents\u2014helping to ensure you stay ahead of issues with precision and speed.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2537\" height=\"1731\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Tracing-Gif.gif\" alt=\"Screenshot of Azure AI Foundry tracing dashboard, showing a list of agent evaluation results with input, output, evaluation metrics, and timestamps for monitoring and debugging AI agent performance. \" class=\"wp-image-45850\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"has-large-font-size wp-block-paragraph\"><em>Security is paramount for our large enterprise customers, and our collaboration with Microsoft allays any concerns. With Azure AI Foundry, we have the desired observability and control over our infrastructure and can deliver a highly secure environment to our customers.<\/em><\/p>\n<cite>\u2014<a href=\"https:\/\/www.microsoft.com\/en\/customers\/story\/23681-spotfire-azure-ai-foundry\" target=\"_blank\" rel=\"noreferrer noopener\">Ahmad Fattahi, Sr. Director, Data Science, Spotfire<\/a><\/cite><\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"get-started-with-azure-ai-foundry-for-end-to-end-agent-observability\">Get started with Azure AI Foundry for end-to-end agent observability<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To summarize, traditional observability includes metrics, logs, and traces. Agent Observability needs metrics, traces, logs, evaluations, and governance for full visibility. Azure AI Foundry Observability is a unified solution for&nbsp;agent governance, evaluation, tracing, and monitoring\u2014all built into your AI development&nbsp;lifecycle. With tools like the Agents Playground, smooth CI\/CD, and governance integrations, Azure AI Foundry Observability empowers teams to ensure their AI agents are reliable, safe, and production ready. Learn more about <a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/ai-foundry\/concepts\/observability\" target=\"_blank\" rel=\"noreferrer noopener\">Azure AI Foundry Observability<\/a> and get full visibility into your agents today!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-s-next\">What\u2019s next<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In part four of the&nbsp;<a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/tag\/agent-factory\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Agent Factory<\/em>&nbsp;series<\/a>, we\u2019ll focus on how you can go from prototype to production faster with developer tools and rapid agent development.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Did you miss these posts in the series?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/agent-factory-the-new-era-of-agentic-ai-common-use-cases-and-design-patterns\/\" target=\"_blank\" rel=\"noreferrer noopener\">Agent Factory: The new era of agentic AI\u2014common use cases and design patterns.<\/a><\/li>\n\n\n\n<li class=\"wp-block-list-item\"><a href=\"https:\/\/azure.microsoft.com\/en-us\/blog\/agent-factory-building-your-first-ai-agent-with-the-tools-to-deliver-real-world-outcomes\/\" target=\"_blank\" rel=\"noreferrer noopener\">Agent Factory: Building your first AI agent with the tools to deliver real-world outcomes<\/a>.<\/li>\n<\/ul>\n\n\n\n<aside class=\"cta-block cta-block--align-left cta-block--has-image wp-block-msx-cta\" data-bi-an=\"CTA Block\">\n\t<div class=\"cta-block__content\">\n\t\t\t\t\t<div class=\"cta-block__image-container\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-1024x768.jpg\" class=\"cta-block__image\" alt=\"A close up of a group of 3 D dev tools.\" srcset=\"https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-1024x768.jpg 1024w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-300x225.jpg 300w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-768x576.jpg 768w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-1536x1152.jpg 1536w, https:\/\/azure.microsoft.com\/en-us\/blog\/wp-content\/uploads\/2025\/08\/Azure-DevTools-Light-2-2048x1536.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/>\t\t\t<\/div>\n\t\t\n\t\t<div class=\"cta-block__body\">\n\t\t\t<h2 class=\"cta-block__headline\">Azure AI Foundry<\/h2>\n\t\t\t<p class=\"cta-block__text\">Build adaptable AI agents that automate tasks and enhance user experiences.<\/p>\n\t\t\t\t\t\t\t<div class=\"cta-block__actions\">\n\t\t\t\t\t<a\n\t\t\t\t\t\thref=\"https:\/\/azure.microsoft.com\/en-us\/products\/ai-foundry\"\n\t\t\t\t\t\tclass=\"btn cta-block__link btn-link\"\n\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\tLearn more\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/aside>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p class=\"wp-block-paragraph\"><em>*The views reflected in this publication are the views of the speaker and do not necessarily reflect the views of the global EY organization or its member firms.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ensuring the reliability, safety, and performance of AI agents is critical. That\u2019s where agent observability comes 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