AI Agent Traces Transition from Telemetry to Application Data

The Evolution of AI Agent Traces

When an AI agent performs a task, such as fixing a failing test, it generates a detailed record of its actions, including files touched, commands executed, and results. Initially, these records might be treated as internal diagnostic telemetry, which can be sampled or discarded.

However, as developers and reviewers need to inspect these activities for verification, debugging, or auditing, these traces transition into application data. This requires a durable version of the run that can be accessed and reviewed over time.

Impact on Data Storage and Management

The reclassification of agent traces as application data significantly alters storage decisions. Unlike telemetry, which often has flexible retention policies, application data requires persistent storage and adherence to the application's access model.

This change is crucial because agent behavior can be nondeterministic and evolve, necessitating aggregated execution data across runs for engineering teams to track performance and improvements.

Product View and Audit Requirements

For product purposes, the execution record does not need to expose the AI model's internal chain of thought. Instead, a projection of observable execution, showing model invocations, tool calls, command results, and state transitions, is sufficient.

This observable record allows users to verify results and build trust in the agent's actions. In certain workflows, these records also serve as audit trails, introducing specific access and retention requirements.