查找技能

Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.

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• Investigating agent latency and execution errors
• Configuring prompt and response logging
• Analyzing agent events in BigQuery

Evaluate AI agents by preparing test cases, scoring responses and tool use, investigating failures, and comparing results after changes. Supports built-in checks and custom scoring rules, including evaluations performed by a language model.

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• Create test cases for an AI agent
• Check response quality and tool use
• Investigate failed evaluation cases

Create starter projects for AI agents, add deployment configuration and automated build and release pipelines to existing projects, or update generated project files. Supports Python and Go ADK templates and a prototype-first approach.

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• Starting a Python or Go agent prototype
• Adding deployment configuration to an existing project
• Adding an automated build and release pipeline

Plan and carry out agent deployments on Google Cloud using agents-cli. The guide covers deployment targets, permissions, secrets, automated build and deployment pipelines, verification, and recovery procedures for agents built with supported frameworks.

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• Comparing Agent Runtime, Cloud Run, and GKE
• Setting up automated build and deployment pipelines
• Configuring secrets and service account permissions