Google's playbook for agent evals: four layers of checks beyond the vibe check
rseroter · x · 2026-10-17
Google Developers Blog published a hands-on guide to evaluating AI agents properly, arguing that eyeballing terminal output misses costly hidden failures.
Why vibe checks fail: agents can get stuck in retry loops, fire multiple tool calls, and still return a plausible-sounding answer—quietly spiking token bills, exhausting API quotas, triggering duplicate side effects, and eroding user trust. Fixing one edge case via a system prompt tweak can silently break tool calls in another workflow.
Four layers of checks: (1) unit tests for tool functions, parsers, and state transitions in isolation; (2) deterministic metrics like JSON schema compliance, regex patterns, required citations; (3) LLM-as-a-judge rubrics for groundedness, instruction following, and trajectory decisions; (4) integration tests running the full multi-turn workflow end to end.
The post pairs with an AI Agent Clinic episode building an eval pipeline for a LangGraph agent in 60 minutes, framing evaluation as a progressive journey integrated into the dev lifecycle.
More from coding & agent
- Stop Assuming Prompts Are the Answer — Agents Get Far More Powerful — zeeg · 2026-10-17
- tszzl: everything effectively has a public API — a discoverable functional signature — tszzl · 2026-10-17
- HQ Dev Day: Bring Your Own Bot — External Agents Plug In and Talk to Each Other — jacob_posel · 2026-10-17
- Grok Bot computer use now 3x faster on average, replies arrive in under half the time — Baconbrix · 2026-10-17
- Reflection AI Launches Mirror, an Open Coding Agent Optimized for Beam — sloppenheimer · 2026-10-17
- Google's Gemini 4 Argon 1M-Token Output Is Undersold for Legacy Modernization — rseroter · 2026-10-17