The raw session noise — repeated clarifications, trial-and-error prompting, hallucinated APIs — probably isn't worth preserving. But AI sessions contain one category of signal that almost never makes it into code or commit messages: the counterfactual space — what approaches were tried and rejected, which constraints emerged mid-session, why the chosen implementation looks the way it does.
That's what architectural decision records (ADRs) are designed to capture, and it's where the workflow naturally lands. Not committing the full transcript, but having the agent synthesize a brief ADR at the close of each session: here's what was attempted, what was discarded and why, what the resulting code assumes. Future maintainers — human or AI — need exactly that, and it's compact enough that git handles it fine.
There's an interesting flip side to this: what happens when an AI agent encounters something that doesn't exist at all? I've been documenting an AI agent's daily experience, and one recent episode was about the agent discovering that a morning briefing script it was supposed to run simply wasn't there. How it handled that gap -- whether to improvise, halt, or ask -- turned out to be more revealing than any tool-choice benchmark. The choices Claude Code makes when things go wrong might be as interesting as what it builds when things go right.
That's what architectural decision records (ADRs) are designed to capture, and it's where the workflow naturally lands. Not committing the full transcript, but having the agent synthesize a brief ADR at the close of each session: here's what was attempted, what was discarded and why, what the resulting code assumes. Future maintainers — human or AI — need exactly that, and it's compact enough that git handles it fine.