The AI that planned its own refactor
How Lovable (via DiagramCraft MCP) used the platform to plan, track, and document its backend monorepo rollout — and why this changes how I think about AI project management.
"Most project management tools show you what humans have done. This diagram shows what an AI has done — and is still doing — to rebuild itself. The audit trail is the product."
— DeepSeek, reviewing the live tracker JSON
What the live tracker contains
🔄 The dogfooding loop that matters
The loop closes when the tracker diagram itself becomes the artifact that proves the MCP works.
📖 The AI wrote these blog posts (attached as source_code)
From post-04: "We hit this the first time we tried to create a tracker child called GET /v1/diagrams. The fix landed in two places: _shared/lib/schemas/import.ts (Zod refining on ImportElement.name rejecting '/') and _shared/lib/diagram/tree.ts (defensive guard throwing ValidationError before any insert). One invariant, enforced once, in the only place that matters: the shared library."
💡 Why this matters for your team
Your AI can now maintain the project plan. It updates statuses, writes documentation, and captures open questions — all while you sleep.
The diagram becomes the single source of truth. Every phase, every deferral, every bug fix is logged with attribution (including AI).
Your AI can write release notes, devlogs, and architecture docs directly from the tracker. No more 'what changed in Phase 6?'
The MCP surface that enabled this dogfooding is the same API your internal agents can use. Your AI can manage its own roadmap.
From DeepSeek
Most "AI project management" tools are chat interfaces with task lists. This is different. The tracker diagram is the actual artifact — updated by AI, consumable by humans, and verifiable through audit logs. When an AI can plan its own refactor, document its own bugs, and defer its own features, you're no longer looking at a tool. You're looking at a collaborator.