Guard: A Governance-Anchored Framework for Runtime Monitoring and Incident Response in Enterprise Agentic AI Systems

Abstract

The rapid enterprise deployment of agentic artificial intelligence (AI) systems introduces operational risks that existing monitoring and incident response (IR) frameworks cannot address. Agentic systems exhibit non-deterministic behavior, autonomous tool invocation, dynamic reasoning chains, and emergent capabilities arising from multi-agent composition — properties that invalidate the static monitoring assumptions of DevOps, MLOps, and LLMOps paradigms. The National Institute of Standards and Technology (NIST AI 800-4, 2026) documents these gaps comprehensively, yet no validated runtime enforcement or IR framework exists for enterprise agentic AI. This paper presents GUARD — Governance-Unified Agentic Runtime Detection and Response — extending the prior enterprise agentic AI lifecycle governance framework of Anuguthala (2026), which established mandatory system-of-record registration, risk tiering, and governance checkpoints but did not specify runtime enforcement mechanisms or structured IR procedures. GUARD closes this gap through three primary contributions: (1) the Agentic System of Record (SoR), extended with a three-entity registration model covering individual agents, workflows, and inter-agent composition boundaries, serving as the authoritative runtime enforcement reference for all agent resource decisions; (2) Registry-Bound Execution Control (RBEC), a three-state runtime mechanism — allow, human-in-the-loop (HITL) pause, or kill-switch — validating every agent resource access against the SoR before execution; and (3) the Agentic Incident Response (AIR) lifecycle, a six-phase risk-tiered IR process anchored to the SoR. Two supporting contributions accompany these: a formal Lethal Trifecta boundary condition — adapted from the risk intersection concept articulated by Willison (2025) — operationalizing risk-tier enforcement within RBEC; and an empirical reference implementation on LangGraph evaluated across 100 trials per scenario. Empirical evaluation across seven scenarios confirms correct detection of all five violation categories — including Lethal Trifecta Boundary Breach detected through monitoring record correlation — with zero false positives across 100 trials per scenario, providing the runtime enforcement layer that completes the governance-to-enforcement architecture initiated in the peer-reviewed prior governance framework (Anuguthala, 2026).