How to build continuous evaluation for AI agents with trace classifications (2026)

Continuous evaluation is a critical process for ensuring the quality and reliability of AI agents after they are deployed in production environments. Unlike pre-deployment or CI evaluations, which rely on predefined test cases, continuous evaluation automatically scores live production traces based on classifications like Task, Sentiment, and Issues using BraintrustTopics. This approach helps identify failures and edge cases that were not anticipated during initial testing phases. By utilizing a system of classifiers and predicates, production failures can be consistently monitored without the need for a scorer per failure mode, thereby reducing maintenance complexity. Scored traces can trigger alerts, initiate review processes, or be promoted to regression tests, ensuring that production failures contribute to ongoing quality improvement. The process is designed to be scalable, with adjustable sampling rates to manage scorer load and costs, and it supports the integration of both built-in and custom facets for more tailored evaluations. This continuous evaluation framework allows for proactive monitoring and faster response to issues, ultimately enhancing the robustness of AI agents in real-world applications.