Agent observability needs feedback to power learning

Agent observability is often initially perceived as a debugging tool, but its true potential lies in facilitating learning and improvement across the entire agent system. This process requires not only traces, which document what an agent did, but also feedback, which provides context and evaluation of the agent's actions. Feedback can come from direct user interactions, indirect user behaviors, or automated evaluations such as rules and LLM-as-judge systems. Effective agent observability platforms must store and integrate traces and feedback, allowing teams to analyze and enhance models, harnesses, and contexts. By doing so, observability transitions from simply recording actions to enabling systematic learning and development, transforming agent traces from mere logs into a comprehensive learning system.