AI Logs API.
Events that explain model behavior.

Give AI behavior an observable structure

AI logging should connect an application request to the work performed on its behalf. Begin with the workflow name, request identity, model attempt, and application outcome. Add events where something meaningful changes: preparation finishes, an invocation ends, validation fails, or a fallback is selected. These boundaries help an investigator understand a request without reading its full input and output.

Keep the vocabulary small and documented. A label such as “completed” should identify what actually completed. The network exchange, the model generation, and the application task can reach different outcomes, so preserve those distinctions when they affect an investigation.

Build a shared envelope and safe attributes

Use consistent identifiers, timestamps, field types, and schema versions across AI workflows. Attribute names should tell a reader what was measured and, where relevant, its unit. Keep unknown values distinguishable from zero or success. The recommended fields below are starting points for an application contract; they are not a required universal format.

Start with metadata that serves a defined purpose. Prompt versions, input classifications, output requirements, and validation codes often explain a change without storing customer content. Review automatic instrumentation as well as application code, since headers, exceptions, and debug output can introduce unexpected copies of sensitive material.

Test the record with a real question

Use synthetic requests to exercise success, invalid output, a recovered retry, and collection failure. Ask whether someone can reconstruct the overall outcome and identify the last confirmed stage. Then verify that exported events contain only the intended fields and that collection duplicates do not appear as extra operations.

Read the practical AI event design guide for a complete walkthrough of lifecycle events, provider mappings, identifiers, and delivery behavior. Build the first useful investigation before expanding the schema.