What problem does it solve?
Evaluation of agent systems requires different approaches than traditional software or even standard language model applications. Agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Effective evaluation must account for these characteristics while providing actionable feedback. A robust evaluation framework enables continuous improvement, catches regressions, and validates that context engineering choices achieve intended effects.
Core Features & Use Cases
- Evaluation Rubrics: Define multi-dimensional rubrics that capture factors such as factual accuracy, completeness, citation accuracy, source quality, and tool efficiency.
- LLM-as-Judge & Human Evaluation: Use scalable LLM-based judgments complemented by human review to validate agent outputs.
- Continuous Improvement: Build gated pipelines that track performance over time and compare configurations.
Quick Start
Use this skill to construct an evaluation suite and run it on a sample agent configuration. For instance, compare agent results against a ground-truth rubric and produce a scored report.
