What is agent observability? Tracing tool calls, memory, and multi-step reasoning

Agent observability is crucial for ensuring the reliability of AI systems as they navigate multi-step workflows, offering insights into every stage of an agent's task execution by capturing tool calls, memory accesses, and decision points. Unlike standard LLM observability, which focuses on individual model calls, agent observability provides a comprehensive view of the entire execution flow, allowing teams to trace errors back to their origins rather than just seeing the final outcome. This capability is essential for debugging complex workflows where failures can emerge from various steps, such as incorrect tool arguments or outdated memory retrievals. Braintrust facilitates this process by offering infrastructure that integrates tracing, evaluation, and CI enforcement into a cohesive workflow, enabling teams to monitor quality, identify failure points, and maintain control over production AI agents. By logging execution paths and linking them to measurable quality signals, Braintrust ensures that teams can investigate issues effectively and apply improvements confidently, with major companies like Dropbox, Stripe, and Zapier leveraging these tools to maintain observability in their AI systems.