Design evaluation frameworks for multi-agent solutions with Microsoft Foundry
Advanced
AI Engineer
Data Scientist
Solution Architect
Azure
Microsoft Foundry
Design evaluation frameworks for production multi-agent AI systems using Microsoft Foundry. Define success metrics that capture coordination quality and system-level outcomes, implement calibrated LLM-as-judge patterns designed for multi-agent chain assessment, design synthetic test datasets that comprehensively exercise agent collaboration scenarios, and build regression testing pipelines for behavioral drift detection.
Learning objectives
By the end of this module, you're able to:
- Define multi-agent success metrics that capture coordination quality, handoff effectiveness, and system-level outcomes
- Implement calibrated LLM-as-judge patterns designed for evaluating complex multi-agent chain quality
- Design synthetic test datasets that comprehensively exercise multi-agent collaboration scenarios and edge cases
- Build regression testing pipelines that detect behavioral drift across agent and model updates
Prerequisites
Before starting this module, you should have:
- Experience designing and running evaluation experiments in Microsoft Foundry
- Familiarity with the Microsoft Foundry Evaluation SDK and built-in evaluators
- Experience from the Evaluate and optimize AI agents through structured experiments module or equivalent
- Experience building multi-agent systems with Microsoft Foundry
- Proficiency in Python
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