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Summary

Congratulations! You have completed the CI/CD for Agentic AI learning path.

What you learned

  • Google ADK agents use a sequential pattern where sub-agents run in order, each handling its part of the request.
  • Monocle test frameworks (MonocleValidator and Fluent API) let you write assertions against agent behavior — which tools were called, which agents were invoked, and what the output contained.
  • Negative assertions are critical for AI agents — testing what the agent should NOT do is as important as testing what it should do.
  • CI/CD pipelines can be progressively enhanced from basic test execution to full autonomous remediation with AI coding agents.
  • Trace embedding in GitHub issues gives AI agents the context they need to investigate and fix failures.
  • Okahu Cloud evaluations (hallucination, completeness, relevancy) add quality gates to your CI/CD pipeline.

Next steps

  • CI/CD for Non-Agentic Workloads

    Learn how to add zero-code instrumentation to traditional pipelines with YAML configuration.

    Start next learning path

  • Debugging AI Agents with Okahu

    Set up the Okahu VS Code extension to instrument, trace, evaluate, and resolve issues.

    Start learning