How to set up manual review workflows for AI agent traces

AI agents operate through a series of intricate steps rather than producing a single output, which necessitates trace-level manual review to identify execution failures that automated scoring might overlook. This manual review process allows for a detailed examination of each decision made by the agent, such as tool choice, parameter generation, and context retrieval, to uncover hidden errors that could affect performance. Braintrust is highlighted as a comprehensive platform that facilitates this review process by providing timeline and thread views of agent traces, allowing reviewers to attach span-level feedback that includes quality scores, failure tags, and comments, thus offering clear guidance for engineers on specific fixes. By converting these reviewed traces into evaluative datasets and CI/CD quality gates, teams can integrate manual review findings directly into their development workflows, ensuring that production failures are addressed systematically and do not recur. This approach is scalable, as it combines automated scoring of production traffic with targeted manual reviews, thereby reinforcing the reliability of AI agents in high-traffic environments while minimizing the workload for human reviewers.

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