Google Cloud Platform provides a suite of cloud computing services for building, deploying, and managing applications and infrastructure on Google's global network. Developers can learn about cloud-native development, machine learning, and big data analytics to leverage GCP's scalable and reliable cloud infrastructure for their projects.

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A practical guide to replacing ad-hoc 'vibe checks' with continuous evaluation (CE) for production AI agents. Covers two engineering modes: discovery (prompt experimentation) vs. defense (regression testing at scale). Uses a multi-agent Course Creator system built with Google ADK, Cloud Run, and the A2A protocol as a reference implementation. Explains a three-level evaluation taxonomy: computation-based metrics (ROUGE, BLEU, regex), rubric-based LLM-as-a-judge metrics (reference-free and reference-based), and Vertex AI managed autoraters (GROUNDING, SAFETY, TOOL_USE_QUALITY). Details how to build evaluation datasets with prompt/reference/reference_trajectory columns, run parallel async inference against shadow deployments, implement custom tool trajectory metrics (precision, recall, order matching), and wire everything into a CI/CD pipeline with Cloud Build that gates promotion on metric thresholds. Also covers distributed tracing with OpenTelemetry and Cloud Trace to debug non-deterministic multi-agent failures.

From "Vibe Checks" to Continuous Evaluation: Engineering Reliable AI Agents

4. The Fuel: Designing Your Evaluation Dataset