Evaluating AI Agents with Google ADK

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.

Learning objectives
Architect eval‑ready AI agents using structured tool interfaces, Pydantic schemas, and reproducible ADK project templates through hands‑on GitHub Codespaces labs.
Audit and debug agent reasoning using ADK trace viewers, trajectory matching, and reusable Golden Traces through interactive code walkthroughs and failure‑mode demos.
Evaluate agents at scale by running headless batch evaluations, Pass@k reliability tests, and statistical performance metrics through live CLI demonstrations.
Implement production safety patterns—LLM‑as‑a‑judge rubrics, groundedness checks, negative logic guardrails, and CI/CD regression gates—using realistic challenge scenarios and rubric‑coding exercises.