The problem
Why this system needed to exist.
Most AI coding workflows are still organized like conversations. Context drifts, two agents can believe they own the same work, claims are accepted without durable evidence, and a handoff often means pasting a summary into another chat.
That is manageable for a small edit. It becomes dangerous when multiple providers, branches, worktrees, QA roles, recovery paths, and long-running implementation arcs are involved.
The system
How the pieces work together.
The system turns each unit of work into an explicit, stateful workflow: requirement, plan, work packet, implementation, independent QA, verification, and handoff. Agents can change while the contract and evidence remain stable.
A Python control plane manages ownership, branch and worktree expectations, locks, state transitions, immutable receipts, recovery, and blocking checks when identity or evidence does not match. Completion is treated as a verified state, not an agent saying it is done.