Do not treat this objective as a recognition exercise. Practise explaining and applying 'Set up AI solutions in Foundry: Design Azure infrastructure for AI apps and agent-based solutions' in a new scenario, then check your reasoning against the official source. Source context: Read in English Design evaluation frameworks for multi-agent solutions with Microsoft Foundry Module 7 Units Feedback 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 Add 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 Get started with Azure Choose the Azure account that's right for you.
Objective
Set up AI solutions in Foundry: Design Azure infrastructure for AI apps and agent-based solutions