Moving from AI Code Review to the Outer SDLC Loop

AI-assisted software delivery requires more than workflow graphs and code review: it needs an outer control plane that governs durable task state, authority, evidence freshness, retries, side effects, observability, budgets, and human intervention across the full lifecycle from intent through release and learning. The author describes two open-source projects, ThreadLoop and Governed Agent Autonomy Patterns, as complementary systems developed in 2026: the former manages long-lived task lifecycle decisions, audit records, current-revision evidence, and guarded transitions, while the latter governs a single agent execution attempt through permissions, isolation, budgets, verification, and receipts. The piece distinguishes graph runtimes, which handle routing and resumption, from the application-level contracts needed to ensure that approvals, tests, reviews, and other evidence remain tied to the exact code revision, action, policy, and scope they authorize. It argues that agent reasoning can remain nondeterministic, but operational boundaries should be deterministic and auditable, with independent verification and clear separation among execution, evidence production, lifecycle control, and human or release authority. Observability is presented as valuable for inspection and accountability but insufficient as an authorization mechanism, while code review is positioned as important evidence within a broader governed SDLC rather than the final control point.

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