Agent Evaluation Frameworks

Agent Evaluation Frameworks enables systematic testing of agent systems to validate performance, assess context engineering decisions, and track improvements over time. Unlike traditional software testing, agent evaluation must address the unique challenge that agents take multiple valid paths to goals, requiring outcome-focused evaluation rather than step-by-step verification.

The skill addresses the non-deterministic nature of agents by focusing on whether agents achieve right outcomes rather than following specific execution steps. Research identifies three factors explaining 95% of performance variance: token usage (80% of variance), number of tool calls (~10%), and model choice (~5%). This suggests upgrading models provides larger gains than increasing token budgets.

Key features include Multi-Dimensional Rubrics capturing factual accuracy, completeness, citation accuracy, source quality, and tool efficiency—avoiding single-metric obsession. Multiple evaluation methodologies are covered: LLM-as-Judge for scalable assessment, human evaluation to catch edge cases, and end-state evaluation for persistent state mutations. Test Set Stratification ensures coverage spans from simple (single tool call) through very complex scenarios (extended interaction with deep reasoning). Continuous Evaluation establishes automated pipelines tracking metrics over time, with production monitoring through sampling and alerting.

Use cases include testing agent performance systematically, validating context engineering choices, measuring improvements over time, catching regressions before deployment, building quality gates for agent pipelines, comparing different agent configurations, and evaluating production systems continuously.

Implementation guidelines emphasize using multi-dimensional rubrics, evaluating outcomes rather than paths, testing realistic context sizes, running continuous evaluations, supplementing automation with human review, and establishing clear pass/fail thresholds based on specific use cases. Essential for production-grade agent development.