Runtime Security Monitoring and Continuous Assurance for AI Agents: An Integrative Review and Systems-Management Framework

Abstract Large language model (LLM) agents increasingly operate as long-lived software actors that plan, invoke tools, access enterprise data, maintain memory, and delegate work. Their security posture can therefore change after deployment as task context, external content, tools, credentials, memory, model versions, and peer agents change. Pre-deployment evaluation alone cannot establish that an agent remains safe during execution. This paper presents a structured integrative review of runtime verification, continuous security monitoring, AI-agent security benchmarks, runtime guardrails, and autonomous-systems assurance, and synthesizes these literatures into a Runtime Security Monitoring and Continuous Assurance (RSMCA) framework for AI agents. The review distinguishes observability, runtime monitoring, runtime enforcement, and continuous assurance as related but non-equivalent functions. It identifies eight runtime monitoring surfaces: identity and configuration, input/context, task and planning state, tool invocation, data/egress, memory and retrieval, inter-agent delegation, and external dependencies. RSMCA represents each agent action as a correlated security event, evaluates it using complementary deterministic, semantic, verifier-based, and probabilistic monitors, and maps monitor output to graduated responses ranging from allow-with-audit to step-up approval, blocking, quarantine, rollback, and credential revocation. A continuous assurance state tracks monitor coverage, policy freshness, behavior risk, dependency health, incident state, and provenance completeness across time. The framework is designed for network and systems management settings in which agent security must be observable, enforceable, recoverable, and auditable. We conclude with implementation guidance, evaluation metrics, and a research agenda for measurable runtime assurance of autonomous AI services.