Agent Evaluation Framework Builder

Build a repeatable LLM agent evaluation framework by defining evaluation types, constructing reproducible datasets, selecting metrics, and setting up an automated LLM-as-judge process. This provides standardized templates to measure performance, reliability, and safety before production.

Set up LLM-as-judge scoring by configuring automated judging processes and trajectory-based scoring mechanisms for multi-step tasks. This automates the evaluation of complex agent pipelines, ensuring objective measurement of reliability and safety across multiple agent environments.

Yes, you can run AI agent evaluations in CI pipelines using a CI-ready harness. This enables automated regression testing and baseline comparison directly in pull requests, tracking improvements over time across multiple agent environments and pipelines.

Structure AI pipeline evaluation datasets using standardized templates to ensure reproducibility. Define evaluation types and select specific metrics tailored to your LLM workflows, enabling consistent baseline comparison and automated trajectory scoring.

No specific testing environment is required, but you need reproducible datasets and selected metrics to validate LLM agent performance. The framework provides templates adaptable across multiple agent environments, integrating directly into your existing CI pipelines.