Evaluating AI Agents: A Practical Guide with Microsoft Foundry

2026-03-16 · Source: Microsoft Foundry Blog articles · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

Microsoft Foundry offers a practical guide for evaluating AI agents, which are complex systems that take actions, call tools, and make decisions across multiple steps. The platform enables developers to define evaluation datasets, run built-in and custom evaluators, and integrate quality checks into their agent development workflow. An evaluation combines a dataset of test cases, evaluators to score behavior, and tooling for debugging and tracking improvements. Datasets can be manually written, synthetically generated, or curated from production interactions, often requiring multi-turn scenarios for agents. Evaluators can be code-based for deterministic checks or LLM-as-judge for nuanced assessments like tone or tool selection. Foundry supports on-demand, event-driven, and scheduled eval runs, providing traces to debug failures and analysis tools like group failures and run comparisons to identify common patterns and quantify impact.

Key takeaway

For AI Engineers building complex agents, integrating evaluations early and continuously is critical to prevent regressions and ensure agent reliability. You should establish a baseline with initial test cases and evaluators, then progressively automate evaluations into CI/CD pipelines and production monitoring. This approach, supported by tools like Microsoft Foundry, allows you to catch issues before deployment and continuously improve agent performance based on real-world interactions.

Key insights

Early and continuous evaluation is crucial for AI agents to catch regressions and ensure quality.

Principles

• Codify test cases to define quality.
• Match evaluators to agent type.
• Prioritize outcomes over paths.

Method

Define datasets with test cases and expected outcomes, select code-based or LLM-as-judge evaluators, run evaluations, debug with traces, and analyze results to improve agent performance.

In practice

• Start with five test cases to establish a baseline.
• Include safety evaluators for all agents.
• Use production data to enrich datasets.

Topics

• AI Agent Evaluation
• Microsoft Foundry
• LLM-as-judge Evaluators
• CI/CD for AI
• Production Monitoring

Code references

• Azure-Samples/get-started-with-ai-agents
• Azure/azure-sdk-for-python

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

Related on AIssential

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• How Braintrust uses AI agents, evals, and CI to ship better software | Ankur Goyal — AI agents and rigorous evals encode "taste" and success criteria, enabling automated, high-quality software development.
• Driving Efficiency in Modern System Engineering with AI Agents — AI agents embedded in engineering workflows enhance efficiency by automating repetitive tasks and augmenting decision-making.
• Your Agent Passed Every Test. It's Still Going to Break in Production. — AgentOps requires a continuous evaluation loop to manage non-deterministic AI agent behavior in production.
• AI Agent Evaluation: Building Reliable Systems Beyond Simple Testing — Agent evaluation must assess multi-step processes, not just final outputs, due to compounding errors and non-determinism.
• Mind the Gap (In your Agent Observability) — Amy Boyd & Nitya Narasimhan, Microsoft — Effective AI agent observability requires continuous evaluation, monitoring, and automated optimization to manage non-determinism and ensure reliability.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Microsoft Foundry Blog articles.