google-agents-cli-eval

Evaluating multi-turn agent conversations requires a framework that analyzes conversation quality, tool usage, and overall performance. You can assess agents against predefined scenarios using relevant evaluation datasets to measure effectiveness.

Yes, defining custom metrics for agent evaluation allows you to tailor assessments to specific scenarios. This capability helps measure unique conversation qualities and tool usage patterns relevant to your ADK agents.

Generating realistic user simulation scenarios involves creating dynamic interactions for agent testing. This approach provides in-depth assessments by mimicking real user behaviors during the evaluation of ADK agents.

Yes, you need the agents-cli tool to run agent performance analysis. It is a required dependency for executing evaluation commands and generating the necessary evaluation datasets for your ADK agents.

Creating evaluation datasets for ADK agents involves running the agents-cli eval generate command. This produces the foundational data needed to compare agent responses against predefined scenarios.

Optimizing ADK agent performance after evaluation involves using dedicated optimization tools. These tools help enhance overall agent quality based on the detailed analysis of conversation and tool usage metrics.