What does a good AI agent trace look like?

Useful AI agent traces enable engineers unfamiliar with an agent to reconstruct a run, identify why it produced an outcome, and isolate performance or reliability problems through clearly structured spans. Each meaningful model call, tool invocation, retrieval operation, retry, handoff, and state update should have stable descriptive names, appropriate types, consistent parent-child relationships, and preserved inputs, outputs, arguments, raw results, model context, configuration, timing, token usage, costs, and errors. The order-status example illustrates how complete tracing can distinguish a model’s incorrect selection of a US returns policy for an order shipping to Germany from a separate carrier-service timeout and retry. Searchable metadata such as user and session IDs, environment, request context, feature flags, and prompt, model, and agent versions helps teams find related production runs and compare behavior across releases. The guidance also emphasizes tracing custom and cross-service work, preserving context during parallel execution, recording failures even when retries or fallbacks succeed, masking sensitive data while retaining useful structure, and marking truncation or attachment limits. Braintrust’s trace viewer supports investigation through span hierarchies, timelines, conversational threads, raw records, filters, and debugging tools, while acknowledging that traces provide observable evidence around model decisions rather than complete access to internal reasoning.

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