What is agent evaluation? How to test agents with tasks, simulations, and success criteria

AI agents, unlike single-response models, execute multi-step actions that interact with external systems, requiring comprehensive evaluation to ensure reliability. Agent evaluation, distinct from single-turn large language model (LLM) assessments, focuses on both the final outcome and the sequence of decisions within a workflow, identifying errors that might affect subsequent steps. It involves end-to-end testing to determine if agents achieve intended goals and step-level analysis to assess decision accuracy and tool use. The process accounts for non-deterministic behavior by running multiple trials, ensuring stable pass rates. Braintrust offers an integrated workflow for agent evaluation from development to production, supporting offline evaluations using stubbed data, simulations, and sandboxed environments to replicate realistic scenarios. Success criteria are defined with measurable outcomes, employing code-based, model-based, and human graders to evaluate performance. By providing a continuous evaluation pipeline, Braintrust helps teams maintain agent reliability, enforce quality standards via CI/CD integration, and adapt to evolving workflows.