software-project-governance

As AI coding agents take on a growing share of delivery work, a quiet risk creeps in: changes lack a traceable evidence trail, reviews become performative rather than substantive, stage gates dissolve into fragments of chat memory, and release decisions get pushed forward before the picture is complete. Software Project Governance is a delivery-trust workflow built to close that gap. It attaches a factual record to every meaningful task — what changed, why, how it was verified, and what remains risky — and wires stage gates, task status, risk items, decision logs, and release readiness into a single auditable loop instead of relying on an agent short-term memory or a human verbal handoff.

Three capabilities stand out. First, evidence-backed delivery tracking: reviewers can verify facts line by line rather than trust a generated summary. Second, explicit gate and risk control: the question of whether a stage can advance becomes a transparent judgment grounded in state, open risks, and recorded decisions. Third, honest reviewer separation and degraded-mode labeling: the workflow distinguishes genuine independent review from execution that depends on a specific runtime or environment, and it does not present every agent as fully supported. New users can initialize the governance directory and let the agent restore state, inspect gates, and surface only critical decisions in under five minutes.

It is best suited for three audiences: tech leads who need to demonstrate AI delivery trust to marketplace or organizational reviewers; engineering teams that juggle multiple AI coding agents (Claude Code, Codex, Gemini CLI, DeepSeek Harness, and others) and want a unified governance surface; and independent developers who are tired of the agent says it is done so we trust it and want a repeatable, auditable, and honestly degraded delivery pipeline.