agent-evals-and-observability

Design an AI agent evaluation framework by defining task and trajectory contracts that measure agent behavior against specific criteria. This process includes dataset versioning, grader calibration, and trajectory review to ensure production-ready reliability.

Add observability to AI agents using privacy-aware telemetry that provides diagnostic context without exposing sensitive user data or prompts. This instrumentation captures trajectory data and behavioral metrics for production-incident learning while maintaining privacy.

A trajectory contract in AI agent evaluation defines the specific behavioral criteria and steps an agent must follow during task execution. It replaces vague metrics with measurable benchmarks for grader calibration and trajectory review.

Yes, this evaluation methodology is framework-neutral and satisfies requirements for statistical rigor across diverse agent architectures. It covers the full lifecycle from task contract definition to production-incident learning without tying to a specific platform.