One connected system for agent observability

Braintrust has introduced Patterns and Debugger, alongside an enhanced Loop experience, to create a connected active-observability system for understanding and improving AI agents in production. Building on Topics, which classifies large trace volumes into broad themes, Patterns continuously identifies recurring failures or opportunities, provides supporting evidence and recommended actions, and can send findings to Slack for team review. Loop enables users to investigate production questions across traces, logs, datasets, and experiments, automate recurring analyses, and, with approval, create evaluators, datasets, dashboards, monitors, and other project assets; its capabilities are also available through a public MCP for compatible coding agents. Debugger focuses on individual complex traces, examining spans, tool calls, results, and model outputs to identify evidence-based likely failure modes and suggest next investigations. Patterns, Debugger, and Loop are available in public preview, with pricing planned before general availability.

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