How to discover hidden failure patterns in your AI agent's production traffic (2026)

The text outlines a methodology for identifying hidden failure patterns in AI agents' production traffic using clustering techniques facilitated by Braintrust Topics. It explains that traditional scorers and alerts often miss failures that aren't predefined, such as stale context or tool retries, due to their reliance on known failure descriptions. The approach involves clustering production traces by behavior, reviewing unusual clusters to confirm failure patterns, and operationalizing confirmed patterns into scorers, evaluation datasets, or review workflows. Braintrust Topics automates the clustering of production traces, categorizing them into behavior clusters that help teams discover failure modes that standard monitoring might overlook. The guide emphasizes the importance of validating cluster findings through a detailed review of traces and understanding user sentiment to prioritize issues that impact user experience. It also discusses the use of custom facets for domain-specific failure tracking and highlights the iterative nature of failure discovery as agents and production environments evolve.

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