Google published a guide on building evaluation pipelines for AI agents, arguing that ad-hoc "vibe checks" miss silent failures like retry loops, hallucinated tool calls, and token burn. It recommends anchoring tests in critical user journeys, using boolean pass/fail rubrics instead of 1-to-5 LLM judge scales, tracking latency/token/cost metrics alongside answer quality, and standardizing OpenTelemetry traces via OpenInference so evals survive framework changes. The open-source agent-eval toolkit pairs trace capture with the Agent Evaluation service on the Gemini platform (formerly Vertex AI…
Read the full story on developers.googleblog.com
What to doConsider adopting a staged eval pipeline (synthetic scenarios, red-pen trace review, then CI/CD gates) and the agent-eval toolkit to catch silent agent regressions before shipping.
- Provider