How to keep AI-assisted codebases consistent

Inconsistency begins with missing context

A broad goal without a source of truth invites parallel data models, duplicate validation paths and new abstractions that conflict with existing ones. The problem is not unique to any agent. Make previous decisions visible: module boundaries, data ownership and where authorization is enforced.

For example, if project access is scoped in a repository layer, a new route must not query the database directly and bypass the owner predicate. Reuse the boundary and test both the rightful owner and a foreign user.

• Name the single authority for sensitive decisions.
• Identify files and modules within scope.
• List behavior that must remain unchanged.

Work in reviewable slices

Start each slice by inspecting the current path and end it with a specific check. Ask for justification before adding a dependency, migration or network call. Review the diff after each slice; a visual polish change should not quietly carry an authorization change.

Tests cannot prove everything, but they protect known boundaries. Include rejection, bounded-data and expected-failure cases for work involving accounts, repositories or deployment.

• Extend existing behavior before introducing a new pattern.
• Test ownership and stale-source boundaries.
• Stop when a decision needs new owner authorization.

Review decisions as well as code

If a rule changes, update the technical foundation, rationale and tests together. Do not let an old document override current production reality. ArchQore keeps Blueprint, brief, readiness and authorization separate so the human decision point remains visible before consequential action.

• Is there still one source of truth?
• Are forbidden capabilities still disabled?
• Can checks run against the final tree?