How Do You Take Back Control of AI-Generated Code?
AI-generated code has a strange failure mode: it can be functionally correct and still unaccountable. Nothing connects the requirement to the implementation. Git records what changed; nothing records why the code exists at all.
My take: control doesn’t come from restricting the agent — it comes from a construction in which the machine simply has no way to change the system silently. Machine-readable contract blocks ahead of the code (Eiffel-style, but used prospectively), tests derived from contracts rather than implementations, explicit gap-marking when the agent lacks context, and a deterministic graph that answers “why does this line exist?”
That construction became LynxContract — an open-source contract annotation language with a graph index, LSP support, and MCP tools for querying provenance. The question shifts from “can AI write this?” to “can we verify why it wrote exactly this?” — which is the question that actually matters.