The SAVE method: Learn the fundamentals to engineer trustworthy generative media
cloud.google.com·4h ago
Cloud & Infrastructure
Google Cloud Blog·4h ago·5 min read
Four evaluation engineering lessons learned from building agent plugins
TL;DR
Building the Google Cloud Developer plugin highlighted the importance of evaluation engineering in AI systems, emphasizing systematic testing to ensure reliability.
✦ Why It Matters
Implement systematic evaluation processes in your AI projects to enhance reliability and user trust.
Key Takeaways
- Grade agents based on execution steps, not just final outputs.
- Design evaluations to capture asynchronous operations accurately.
- Run live and plan-only evaluations side by side.
- Use golden sets as benchmarks for agent performance.
How It Works
Evaluation engineering involves creating systematic tests that measure an agent's behavior throughout its execution. By analyzing tool-call trajectories and preserving turn structures, engineers can identify when an agent's actions deviate from expected behavior.
This approach allows for a more accurate assessment of the agent's capabilities and decision-making processes.