AI coding agents can produce code that compiles, passes tests, and still implements the wrong product because an underspecified request was silently turned into a plausible assumption. NoGuess is an epistemic control plane for AI coding agents built around IBM Bob. It sits between a developer request and implementation. Bob can propose plausible interpretations of an ambiguous requirement, but NoGuess compares their observable effects rather than trusting wording or model confidence. If two plausible interpretations would produce materially different behavior, NoGuess records a semantic fork as Guess Debt and denies implementation capability. The system then asks the smallest neutral human question that can distinguish the competing behaviors. A model proposal cannot declare ambiguity resolved, manufacture a human decision, or grant itself execution authority. Only an allowed evidence path—such as an explicit human decision—can resolve the relevant Guess Debt. A deterministic kernel then grants or denies capability from that evidence basis. Every accepted transition is recorded in an append-only, hash-chained event ledger. NoGuess produces an Epistemic Receipt showing what was known, what was inferred, where interpretations diverged, what a human actually decided, and why the agent was allowed to act. The flagship demo starts with: “Make VIP refunds automatic.” With no human answer, the system remains BLOCKED_UNRESOLVED_INTENT. An explicit human selection can move it to READY_FOR_IMPLEMENTATION. Choosing “Other / none of these” returns NEEDS_EXTERNAL_INPUT and stays blocked rather than fabricating closure. The target users are engineering teams using AI coding agents in enterprise, regulated, high-change, or high-cost codebases where a technically valid but wrongly inferred requirement can cause expensive rework or operational risk. The key difference is that NoGuess does not merely ask clarification questions after uncertainty appears; it controls whether implementation authority exists before the agent acts.
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