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

In AI agents, the core decision-making logic shifts from static code to the model's runtime behavior, making traditional software development paradigms insufficient. The true documentation of an agent's actions and reasoning is found in its "traces," which record the sequence of steps taken during execution. This fundamental change requires that key development practices like debugging, testing, and optimization be re-centered around trace analysis instead of code inspection. Building reliable AI systems therefore necessitates robust observability platforms that can capture, search, and analyze these traces to monitor quality and understand agent performance.