Agent Evaluation Cycle overview

Rigorous evaluation workflow for measuring the accuracy, reliability, and safety of AI agent systems across defined benchmark tasks and adversarial scenarios. The ML engineer assembles an evaluation harness with a curated dataset of prompts, expected outputs, and rubric-based scoring functions. The backend engineer integrates the harness into CI so every model or prompt change triggers an automated eval run. Regression thresholds enforce that new versions do not degrade on prior benchmarks, while exploratory eval sessions probe edge cases and failure modes that inform the next iteration of the agent's architecture or system prompt.