How Much Documentation Does an AI Coding Agent Really Need?

Once I started using coding agents more seriously, I became very interested in project documentation. If the agent understands the architecture, product rules and important decisions, surely it will make better changes.

That is true. But there is an obvious trap: documenting everything because AI might need it someday.

For a large, stable project, good architecture notes can save a lot of time. A short document explaining the core domain model, important flows and unusual decisions can stop an agent from rediscovering the same things on every task.

For a brand-new side project, I do not think I need ten Markdown files describing an architecture that may completely change next week.

The code itself is still an important source of context. A capable coding agent can inspect files, search the repository and understand a surprising amount from what already exists. Documentation should fill the gaps that are difficult or expensive to infer from code.

That usually means product rules, architectural decisions, constraints and reasons behind unusual choices.

I also prefer documentation that is short enough to remain accurate. A beautiful architecture document that nobody updates can become worse than no document because an agent may confidently follow information that stopped being true months ago.

So my current approach is to document what is stable and important, not everything that exists.

A new project should earn its documentation as it grows.