AI agents are transforming how organizations automate complex workflows, but deploying them reliably requires rigorous evaluation methods that go beyond traditional testing. In this course, instructor Jigyasa Grover teaches you how to build production-grade AI agents using the Google Agent Development Kit (ADK) with a focus on deterministic evaluation, trace analysis, and safety guardrails. Learn how to design eval-ready architectures using structured tool interfaces and Pydantic schemas, then audit agent reasoning through trajectory matching and Golden Trace baselines.
Jigyasa shows you how to implement scalable benchmarking with headless batch evaluations, Pass@k reliability tests, and LLM-as-a-Judge scoring systems. Explore production safety patterns, including groundedness checks, negative logic guardrails, and CI/CD regression gates that ensure your agents behave reliably at scale. By the end of this course, you’ll be equipped with hands-on experience architecting, debugging, and evaluating AI agents ready for real-world deployment.
