A Survey on Autonomous Compliance Enforcement using Agentic AI

Abstract

Infrastructure compliance enforcement is increasingly considered for agentic AI systems that can plan, act and self-correct over many steps. A structured examination of the literature reveals several significant gaps. Multi-agent compliance pipelines have been architecturally proposed in several works but none report a functioning prototype with measurable compliance outcomes. Theoretical discussions extensively cover the safety hazards arising from the granting of autonomous control to an LLM agent over remediation of infrastructure. However, there are no documented real-world cases of an LLM agent outputting an operationally dangerous output in a compliance setting. The concept of utilising cross-run memory for compliance agents has been recognised but no lightweight implementation has yet been demonstrated to change the behaviour of agents. This paper surveys the field along seven dimensions: agentic architectures, compliance automation, LLM output safety, anomaly detection, multi-agent coordination, statefulness, and cloud-native deployment drawing on 46 representative works. Six specific gaps are identified through structured analysis. A hybrid architecture is then proposed that integrates the Isolation Forest anomaly detection with a four-agent LLM pipeline consisting of an Analyser that interprets system state and prior run history, a Planner that generates remediation strategies, a Verifier that applies LLM safety constraints, and an Explainer that produces human-readable audit reports. The architecture further incorporates deterministic value-level validation and persistent SQLite-based cross-run memory. In controlled experiments, the system improved compliance scores from 60% to 100%. Notably, a concrete instance of LLM overreach was observed during testing: the Planner agent generated a remediation plan that would have locked out SSH access by closing all network ports, a failure mode not previously reported in empirical literature. The paper concludes with a feature-by-feature comparison across twelve prominent works, a discussion of open challenges, and a proposed hybrid cloud extension.