Corrections become rules
A fix you make once is captured as a rule the agent keeps. The same drift stops needing the same correction.
An AI agent is fixed until the next model ships. On its own, it makes the same mistakes next week that it makes today — and when a new model does arrive, it can behave differently enough that you have to recalibrate.
Tapestry watches across all your projects and turns recurring friction into structure — so the work keeps getting better even though the agent stays the same.
Every project hits the same kinds of friction — a correction repeated, a workflow redone, context re-explained. Tapestry captures it once and turns it into structure the agent reuses.
Because solving the same problem twice is where projects quietly lose their momentum.
Why it matters
An agent doesn't learn between releases. The improvement in your work can't come from the agent alone — it comes from what accumulates around it: the corrections, decisions, and patterns captured as structure. That structure survives a model change instead of resetting with it.
That's what makes sure:
Tapestry builds that structure automatically.
What becomes structure
The recurring friction in your work gets captured in a form the agent reuses — automatically.
A fix you make once is captured as a rule the agent keeps. The same drift stops needing the same correction.
Work you do the same way again and again compiles into a named skill, available in every project by reference.
What you decided and why is kept and recalled, instead of re-explained from scratch each session.
Friction that recurs across projects is surfaced and turned into durable structure you reuse everywhere.
The observer watches for what recurs, so it becomes structure before it becomes a recurring cost.