January 6, 2026
Last Updated: January 6, 2026
There's a moment in every AI workflow where things break down. The agent proposes adding a function that already exists three files away. It suggests an architecture that contradicts something you built last week. It confidently navigates to the wrong place.
The models are good enough. This is a context problem.
When you drop an AI agent into a codebase it's blind. It sees the file you're pointing at. But it has no map. It doesn't know the authentication logic lives in three places for historical reasons. Nobody wrote it down. Or someone did and that document is buried in a wiki untouched for eight months.
We built atris because we got tired of re-explaining our own codebases to machines. If agents need context to work well then context should be infrastructure. A living navigation layer that agents reference automatically.
MAP.md is the heart of this. One file that answers the question every agent asks: where is the thing I'm looking for? When an agent reads MAP.md first it stops guessing. It cites specific file:line references instead of making up paths.
The journal protocol handles the human side. Brain dump your thoughts into an inbox. The navigator processes them into tasks with exact file references. The executor builds. The validator checks. Handoffs happen through sections of a markdown file. No orchestration layer. Just text.
Same markdown works for CLI and web and mobile and agents. You can grep it. You can render it. The format is the API.
AI collaboration is fundamentally a context problem. Context problems are solved with structure. Not with bigger models.
The teams that invest in context infrastructure will outperform. They'll have something the others don't. A navigable map of everything they've built.
That compounds. That can't be downloaded. That's the moat.