Constraint-Based Governance of Agentic AI Systems Inference-Time Control Through the Human Recursive Interaction System (HRIS)

The shift from passive language models to agentic systems capable of multi-step reasoning and tool use has revealed persistent failure modes, including hallucinated completion, scope drift, assumption escalation, and state inconsistency, that persist even in highly capable models. This paper proposes extending the Human Recursive Interaction System (HRIS) as an inference-time, constraint-based governance layer for agentic AI systems. Rather than retraining models or relying on post-hoc filters, HRIS enforces a persistent set of epistemic and operational constraints prior to action execution. We formalize an HRIS-augmented agent architecture in which proposed actions are evaluated against a two-kernel constraint set before execution. The epistemic kernel (K1-K5), previously validated in long-horizon conversational settings, governs the quality and grounding of reasoning. The autonomy kernel (K6-K8), introduced here, governs operational risk in the absence of human oversight, covering reversibility gating, scope ceiling enforcement, and escalation threshold management. Together, these eight constraints constitute a complete governance specification for unsupervised agent deployment. We further provide a full implementation blueprint: a defined interface contract, JSON schema, three interchangeable instantiation patterns, and a worked example tracing a single agent step through the complete governance loop. This blueprint is offered as a specification sufficient for a practitioner to operationalize HRIS governance without architectural modification or model retraining. The extension from conversational to agentic contexts is presented as a formal proposal pending empirical validation on benchmarks such as AgentBench and WebArena.