In software, the code documents the app. In AI, the traces do.

In the context of AI agents, traditional software development practices where the code serves as the source of truth are becoming obsolete, as the actual decision-making now happens within the model at runtime. Consequently, the source of truth shifts from the code to the traces, which document the agent's behavior, reasoning, and decision steps. This transformation necessitates a fundamental change in debugging, testing, optimizing, monitoring, and collaboration strategies, as these activities now revolve around trace analysis rather than code analysis. Debugging involves analyzing traces to find reasoning errors, testing requires evaluating traces in production due to the non-deterministic nature of AI agents, and performance optimization focuses on decision patterns within traces. Monitoring must assess the quality of decisions rather than just system uptime, and collaboration shifts to observability platforms where traces are shared and analyzed. Additionally, product analytics merges with debugging because understanding user behavior requires analyzing the agent's decision-making processes documented in the traces. This shift emphasizes the need for robust observability tools to effectively manage and improve AI agents.