Governed Coding and Debugging: A Claim-Governance Grammar for Human–AI Agentic Workflows

A Claim-Governance Grammar for Human–AI Agentic Workflows Governed Coding and Debugging applies the epistemic grammar of Ableman’s Razor to one of the highest-friction modern practices: coding and debugging in human–AI agentic systems. It addresses a core failure of contemporary workflows - where fixes rapidly harden into explanations, explanations into memory, and memory into authority, often without admissible observation or declared scope. The paper introduces a governance layer that constrains when debugging actions may license claims about root cause, correctness, or system guarantees. It enforces jurisdictional typing across Specification, Configuration, and Implementation; legitimizes Undecidable as a terminal outcome under fixed observation policy; introduces a one-move constraint to preserve inference validity; and treats shortcuts as epistemic debt that compounds through reuse rather than time. This framework does not optimize for speed or correctness. Instead, it reduces false causal inference, premature pattern reuse, and agentic thrashing by regulating epistemic authority. In environments where AI agents generate confidence cheaply, these constraints often manifest as improved convergence, fewer regressions, and more durable fixes - not by making agents smarter, but by making failure legible and escalation earned. The paper is designed to be compatible with existing debugging practices and agent architectures, while remaining resistant to ritualization or checklist-based misuse.