How to eval stateful agents

Stateful agents, unlike traditional prompt-response models, retain memory across multiple steps and interactions, enabling them to operate effectively in unstructured and dynamic environments. These agents accumulate context, remember prior actions, and make decisions based on the state of external systems, which can change independently over time. This complexity introduces unique challenges for evaluation, as traditional methods focusing on single-step prompts are inadequate. Proper evaluation requires capturing the agent's state and trajectory over time, including the potential side effects of their actions. Effective stateful evaluations should involve real interaction with systems to capture meaningful state changes, turning real-world failures into test cases for continuous improvement. The process involves observing agent behavior through detailed logging, transforming failures into actionable datasets, and leveraging automated scoring within CI/CD pipelines to prevent regressions. Tools like Braintrust facilitate this by providing observability, scoring mechanisms, and clustering of failure patterns, allowing teams to iteratively refine and ensure the reliability of stateful agents in production environments.