anthropic-evaluations

Design AI agent evaluations by adapting YAML templates like coding-agent-eval.yaml or conversational-agent-eval.yaml, configuring code-based, model-based, or human graders, and running the evaluation harness to collect transcripts and pass/fail signals.

Grader types for benchmarking AI agents include code-based graders, model-based graders, and human graders, which can be configured within YAML templates to track standard metrics and ensure robust QA across coding, conversational, and research tasks.

Yes, you can evaluate conversational agents and coding agents by starting with provided YAML templates, adapting them to your specific task, and configuring the tracked metrics to run the evaluation harness and collect tool usage data.

The best way to structure an evaluation harness for AI research agents is to use Anthropic-style eval patterns, adapting starter YAML templates to define standardized graders, collect transcripts, and track pass/fail signals for rigorous QA.

No external dependencies or libraries are required to use these AI eval templates; you simply review the references directory for evaluation patterns, adapt a template to your task, and configure your graders and tracked metrics.

AI agent evaluations require collecting transcripts and tool usage signals because rigorous graders use this data to determine pass/fail outcomes and benchmark agent capabilities across varying coding, conversational, and research tasks.