Agent Trace RFC

Summary

Agent Trace Version: 0.1.0 Status: RFC Date: January 2026 Abstract Agent Trace is an open specification for tracking AI-generated code. It provides a vendor-neutral format for recording AI contributions alongside human authorship in version-controlled codebases. Table of Contents Motivation Goals Non-Goals Terminology Architecture Overview Core Specification Extensibility Reference Implementation Appendix 1. Motivation As agents write more code, it's important to understand what came from AI versus humans. This attribution is both the models used as well as the related agent conversations. Agent Trace defines an open, interoperable standard for recording this attribution data. 2. Goals Interoperability: Any compliant tool can read and write attribution data. Granularity: Support attribution for models used at file and line granularity. Extensibility: Vendors can add custom metadata without breaking compatibility. Human & Agent Readable: Attribution data is readable without special tooling. 3. Non-Goals Code Ownership: Agent Trace does not track legal ownership or copyright. Training Data Provenance: We don't track what training data influenced AI outputs. Quality Assessment: We don't evaluate whether AI contributions are good or bad. UI Agnostic: Agent Trace does not require any specific interface. 4. Terminology Term Definition Contribution A unit of code change (addition, modification, or deletion) Contributor The entity that produced a contribution (human or AI) Trace Record Metadata describing a contribution's origin Contributor Types Type Code Description Human human Code authored directly by a human developer AI ai Code generated by AI Mixed mixed Human-edited AI output or AI-edited human code Unknown unknown Origin cannot be determined 5. Architecture Overview Agent Trace is a data specification, not a product. It defines how to record attribution data. Storage mechanisms are implementation-defined. The spec is unopinionated about where traces live. 6. Core S...