agentv-eval-builder

You can create YAML evaluation files for AI agent performance testing by declaratively defining test cases, configuring assertion types like code judges and LLM judges, and structuring evaluation suites within an AgentV eval.yaml file.

In an LLM evaluation suite, you can configure assertion types including code judges, LLM judges, and rubrics to score and validate AI agent outputs against expected test scenarios.

You can add, modify, or remove test cases within an existing AI agent YAML evaluation file by declaratively defining new sample inputs and expected outputs alongside your configured assertion parameters.

Yes, you can use YAML to declaratively define evaluation parameters and test scenarios for AI agents, allowing you to structure test cases and configure evaluators without imperative code.

The best way to structure evaluation suites for document summarization agents is to define test cases with sample documents and expected summaries in a YAML file, then configure an LLM judge to automatically score the output.

No external dependencies are required to build AgentV evaluation files, as the Skill operates independently to generate and manage YAML definitions for test cases and assertion configurations.