Hi Product Hunt! 👋 I started building Aming Claw after repeatedly watching capable coding agents fail for reasons that were not really about intelligence. They lost their verified position, reconstructed stale context, or chose among several plausible next actions. Humans then had to act as an expensive GPS. The project began as a timeline and backlog for long-running agent work. Through dogfooding, it evolved into graph-backed governance with independent worker and QA roles, auditable bypasses, and controlled merge/reconcile flows. That process also produced the model behind the product: P(correct step) = P(correct position) × P(correct entrance | position) Aming Claw therefore verifies where the agent is and pushes one legal next entrance instead of asking it to pull and guess its own context. I would especially value real failure cases and counterexamples. Where have your agents lost their place, and does “position + one entrance” explain the failure?

Aming Claw

Governance for reliable, long-running AI agents

Governance for reliable, long-running AI agents