As AI agents become regular participants in software projects, documentation must evolve to serve both human and machine readers. This case study shows how one team converged on a governance plan through real problems, a deliberate experiment, and two rounds of trade-off discussions. The core challenge: helping people and agents quickly understand the current project state while preserving the context of why changes happened, how they were executed, and how they were verified. The resulting framework balances historical context with operational clarity, avoiding the trap of over-documentation. Key insights include treating documentation as a living system, using evidence from actual incidents to drive decisions, and designing for both human readability and agent parseability. Teams adopting AI-assisted workflows will find this a useful reference for their own documentation strategy.
A practical framework for documentation governance that serves both human developers and AI agents, developed through real incidents and iterative experiments.