Your questions about Patterns, Topics, and Loop, answered

Braintrust’s new Patterns, Topics, enhanced Loop, and Debugger features are designed to connect production tracing, evaluation, investigation, and iteration for AI agents in a continuous feedback process. Patterns can automatically identify recurring agent failures from sampled traces even without preconfigured evaluators, investigate their prevalence more broadly, suggest ways to create evals, and be customized using an open-source Agent Behavior Spec; it is currently free in public preview aside from bring-your-own-model-key inference costs. Topics provides real-time trace clustering and nightly cluster regeneration to identify broad themes, while Loop can automate alerts or investigations and Debugger supports analysis of individual traces. The platform supports single-turn, trace-level, span-level, and session-level evaluation, allowing teams to assess outcomes and agent trajectories across multi-turn conversations regardless of whether they organize data as threads or traces. Teams can use discovered failures to create targeted evals, deploy fixes, and monitor whether problems decline over time, including for emerging agent and MCP-server use cases, though telemetry limitations remain for coding-agent interactions.

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