As AI Agents increasingly participate in real-world enterprise workflows, traditional testing approaches — such as simple QA or manual validation — are no longer sufficient. Modern AI Agents contain autonomous capabilities: interpreting user intent, invoking external tools, and synthesizing multi-step actions. These characteristics require a systematic, quantifiable evaluation framework that not only measures correctness, but also robustness, safety, and trustworthiness.
In this session, we will explore how Microsoft’s Azure AI Foundry evaluation ecosystem helps developers build and validate reliable enterprise AI Agents. We cover practical patterns for designing evaluation pipelines that go beyond surface-level QA:
* Defining critical evaluation dimensions such as task success criteria, tool-calling correctness, reasoning validity, and security boundaries.
* Leveraging automated evaluators and benchmark frameworks to run large-scale tests across agent versions and configurations.
* Introducing red-teaming workflows where agents are adversarially challenged to uncover safety failures, injection vectors, and unexpected behaviors.
* Demonstrating how evaluation results can feed into governance processes, enabling better model and agent version decisions before production deployment.
Attendees will leave with concrete insights into engineering repeatable, measurable, and defensible testing strategies for AI Agents — bridging the gap from experimental prototypes to enterprise-grade solutions.
Takeaways:
1. How to structure agent evaluation beyond simple QA tests.
2. How to apply automated and adversarial evaluation to uncover hidden failure modes.
3. How to operationalize agent validation within an enterprise AI lifecycle using Azure tools.