agent-evaluation

You can evaluate agent performance by running a structured evaluation pass that measures trajectory quality and final response accuracy. This requires well-defined reference trajectories, evaluation metrics, and guardrails to ensure safe, reproducible assessments.

Multi-layered agent evaluation assesses tool-use trajectories, final responses, and capabilities using benchmark-driven scoring with golden datasets and autoraters. It provides reproducible, safety-conscious validation for agent workflows in development and production.

Benchmark agent performance by applying benchmark-driven scoring with golden datasets and autoraters to evaluate tool-use trajectories and final responses. This enables reproducible, safety-conscious agent validation and A/B testing for model improvements.

Human-in-the-loop validation is supported through HITL calibration and A/B testing for model improvements. This combines automated evaluation with human oversight to ensure safe, reproducible agent validation in development and production contexts.

Running an agent evaluation pass requires well-defined reference trajectories, evaluation metrics, and guardrails. These inputs ensure safe, reproducible assessments of trajectory quality and final response accuracy across testing and benchmarking scenarios.

Avoid automated agent evaluation when you lack well-defined reference trajectories, evaluation metrics, or guardrails. Without these prerequisites, the framework cannot ensure safe, reproducible assessments of tool-use trajectories and final response quality.