ARGO open-sources a staged agent loop for traceable AI coding delivery
PossibleOrdinary4572 · reddit · 2026-08-04
ARGO turns AI coding delivery into a staged, traceable agent loop
The author built ARGO, an open-source, workspace-installable agent loop for Cursor, GitHub Copilot, and OpenCode after repeatedly seeing larger coding tasks lose the reasoning behind requirements, architecture boundaries, and acceptance criteria across sessions.
- It stores goals, capabilities, constraints, dependencies, work packages, and acceptance tests in a canonical SystemArchitecture.json.
- It uses GraphRAG retrieval plus a staged loop: business clarification → intent design → implementation design → coding/repair → two-level acceptance → archive.
- The two acceptance checks separate implementation contract from original business intent; failures get routed back to the stage that owns the gap.
- The system supports both human-in-the-loop approvals and supervised autonomous runs, but is heavier than a prompt chain and aimed at high-risk, multi-team, or auditable work.
The author asks for feedback on where agent context gets lost, how people distinguish intent vs architecture vs implementation failures, and whether the stage boundaries are too rigid.
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