Unlocking the Power of Agent Observability in AI Development

Ensuring your AI agents are reliable, safe, and perform well is essential. This is where agent observability steps in.

This post is the third in our six-part series called Agent Factory, where we share best practices, design patterns, and tools to support your journey in adopting and building agentic AI.

Grasping the Concept: Why Agent Observability Matters

As AI becomes integral to business processes, it’s crucial to ensure its reliability, safety, and performance. This is the heart of agent observability, which helps teams to:

• Identify and resolve issues early in development.
• Ensure that agents meet quality, safety, and compliance standards.
• Enhance performance and user experience in real-time applications.
• Maintain trust and accountability in AI systems.

With increasingly intricate systems involve numerous agents and various modalities, observability is vital for delivering AI that is effective, transparent, and aligned with your company’s ethics. It allows teams to build confidently and scale responsibly by providing insights into how agents act, make decisions, and respond to real-world scenarios throughout their lifecycle.

Understanding Agent Observability

Agent observability is all about achieving a comprehensive and actionable insight into the inner workings, decisions, and results of AI agents, spanning from design and testing to deployment and ongoing operations. Key elements include:

• Continuous monitoring: Observing agent activities and decisions in real-time to catch any anomalies or unexpected behaviours.
• Tracing: Capturing detailed pathways of execution, understanding how agents reason, select tools, and collaborate with others. This answers not just “what happened,” but “why did it occur?”
• Logging: Keeping a detailed record of agent decisions, tool usages, and internal changes to assist in debugging and analysing behaviour.
• Evaluation: Regularly assessing agent outputs for quality and safety using automated and human oversight.
• Governance: Implementing policies to ensure agents function ethically and in line with both organisational and regulatory standards.

Traditional Observability vs Agent Observability

Standard observability usually rests on three main pillars: metrics, logs, and traces. These components offer insight into system performance, help identify failures, and facilitate root-cause analysis, particularly in traditional software systems where the focus is on infrastructure health, latency, and throughput.

However, AI agents are inherently unpredictable, introducing new factors like autonomy, reasoning, and dynamic decision-making. This demands a more sophisticated observability framework. Agent observability improves on traditional methods by integrating two essential components: evaluations and governance. Evaluations help assess how effectively agents meet user intentions and complete tasks, while governance ensures safe and ethical operation.

This expanded method provides deeper understanding not just of what agents do, but also why and how they perform, supporting continuous monitoring throughout the lifecycle, from development to deployment—essential for building trustworthy, high-performing AI solutions.

Introducing Azure AI Foundry Observability

Azure AI Foundry Observability offers a comprehensive solution for evaluating, monitoring, tracing, and governing the quality and safety of your AI systems throughout Azure AI Foundry, seamlessly integrated into your AI development process. From model selection to real-time debugging, the Foundry’s observability features empower teams to deploy production-grade AI with confidence and speed.

With integrated tools like the Agents Playground evaluations, Azure AI Red Teaming Agent, and Azure Monitor compatibility, Foundry Observability incorporates evaluation and safety into every phase of the agent lifecycle. Teams can trace each agent’s flow with contextual execution data, simulate potential security threats, and monitor live interactions through custom dashboards. Effortless CI/CD integration allows continuous evaluation at every code commit, while governance support from Microsoft Purview, Credo AI, and Saidot simplifies adherence to regulations like the EU AI Act—facilitating the construction of responsible, scalable AI solutions.

Top Five Best Practices for Effective Agent Observability

1. Choose the Right Model Using Benchmark-Driven Leaderboards

Every agent relies on a model, and selecting the right one is crucial for success. While developing your AI agent, you need to identify the most suitable model based on safety, quality, and cost.

To choose wisely, you can either evaluate the model based on your own data or utilise Azure AI Foundry’s model leaderboards. These comparisons let you assess various foundation models based on quality, cost, and performance, all supported by industry benchmarks. This way, you can visualise trade-offs and make informed, data-backed decisions.

2. Continuously Evaluate Agents During Development and Production

AI agents are powerful assistants capable of planning and executing tasks. They typically start by understanding user intents, choosing the right tools, and fulfilling requests. Evaluating their behaviour and performance before deployment is vital.

Azure AI Foundry simplifies agent evaluation with built-in tools for Intent Resolution, Task Adherence, Tool Call Accuracy, and Response Completeness. Beyond these, it offers a suite of evaluators that examine the overall quality, risk, and safety of AI, ensuring standards like relevance, coherence, and fluency. The Foundry’s Agents Playground consolidates these evaluation and tracing tools, making it easy to test, debug, and enhance the agentic AI.

3. Embed Evaluations into Your CI/CD Frameworks

Integrating automated evaluations into your CI/CD pipeline ensures every code modification is scrutinised for quality and safety prior to release. This practice helps catch regressions early, ensuring that agents retain their reliability as they evolve.

Azure AI Foundry works with your CI/CD workflows using platforms like GitHub Actions and Azure DevOps, allowing automatic evaluations on every commit. You can compare versions based on built-in metrics and use confidence intervals to guide decision-making—ensuring that each version is ready for production.

4. Proactively Scan for Vulnerabilities with AI Red Teaming

When it comes to security and safety, it’s crucial to be proactive. Before deployment, run tests on your agents to identify security risks by simulating adversarial scenarios. Red teaming helps reveal vulnerabilities that could be exploited, strengthening overall robustness.

Azure AI Foundry’s AI Red Teaming Agent automates this assessment, measuring risks and generating readiness reports. It enables teams to simulate attacks, validating agent responses and overall workflows to ensure they’re ready for production.

5. Continuously Monitor Agents Post-Deployment

Ongoing monitoring is crucial after deployment to quickly address any issues, performance fluctuations, or regressions. Using evaluations, tracing, and alerts allows for maintaining reliability throughout the agent’s lifecycle.

Azure AI Foundry observability offers continuous monitoring through a unified dashboard powered by Azure Monitor Application Insights and Azure Workbooks, giving you real-time insights on performance, quality, and safety. This way, you can carry out continuous evaluations on live data, set alerts for potential issues, and trace each result for comprehensive observability.

Get Started with Azure AI Foundry for Comprehensive Agent Observability

To wrap up, conventional observability consists of metrics, logs, and traces, whereas agent observability adds evaluations and governance for complete visibility. Azure AI Foundry Observability is a unified tool for governance, evaluation, tracing, and monitoring, all embedded into your AI development journey. Tools like the Agents Playground, seamless CI/CD integration, and governance support ensure your AI agents are safe, reliable, and ready for production. Discover more about Azure AI Foundry Observability and gain deeper insights into your agents today!

What’s Next?

In part four of our Agent Factory series, we’ll explore how to transition from prototype to production more efficiently using development tools and rapid agent creation.

Have you missed previous entries in this series? Catch up below: