customaize-agent:agent-evaluation

To evaluate LLM agents using multi-dimensional rubrics, apply a structured framework measuring factual accuracy, completeness, tool efficiency, reasoning quality, and response coherence to surface actionable feedback and drive continuous prompt improvements.

Bias mitigation in LLM evaluation reduces inconsistent judgments through techniques like position swapping, model cross-checks, and length-normalized scoring. These methods improve reliability when assessing non-deterministic agent responses and context drift.

Test Claude Code agent prompts for context drift by running an initial evaluation pass to establish baseline metrics. Use structured evaluation templates to guide automated and human evaluation across prompt-testing and deployment monitoring.

Yes, this evaluation framework works for multi-agent deployments by providing performance measurement across multiple agents. It applies context-engineering validation and structured prompts to manage non-determinism and ensure consistent judgments.

Evaluating non-deterministic AI agents faces limitations like context drift and inconsistent judgments. Mitigate these constraints by applying bias mitigation techniques and length-normalized scoring to improve reliability during prompt iteration.