A Multi-Agent AI Framework for Autonomous Patch Management

Enterprise patch management has evolved into a highly complex operational and cybersecurity challenge due to increasing infrastructure heterogeneity, accelerated software release cycles, dependency sprawl, and the growing volume of publicly disclosed vulnerabilities. Traditional patch management models, while supported by workflow automation and vulnerability management tools, continue to rely heavily on fragmented human decision-making, manual coordination, delayed testing, and reactive deployment strategies. These limitations increase Mean Time to Remediation (MTTR), prolong vulnerability exposure windows, and elevate operational risk. This paper proposed a Multi-Agent AI Framework for Autonomous Patch Management in Enterprise Systems, introducing a research-oriented architecture that integrates specialized autonomous AI agents across the full patch lifecycle. The framework combines a hybrid infrastructure and DevSecOps perspective, enabling applicability across Linux operating systems, enterprise middleware, application dependency remediation, containerized workloads, and CI/CD-driven software delivery environments. The proposed architecture includes: ·                  Vulnerability Intelligence Agent ·                  Risk Assessment Agent ·                  Dependency Analysis Agent ·                  Testing Orchestration Agent ·                  Deployment Strategy Agent ·                  Monitoring Agent ·                  Rollback Decision Agent ·                  Governance Policy Agent These agents collectively transform traditional patch management from fragmented workflow automation into an intelligent reasoning-driven orchestration model. The conceptual comparative evaluation suggests that the proposed framework can provide meaningful operational improvements, including: ·                  substantial MTTR reduction ·                  reduced manual intervention ·                  stronger regression validation coverage ·                  faster rollback response ·                  improved deployment consistency ·                  enhanced operational resilience ·                  better governance alignment The hybrid architecture is particularly valuable for enterprise environments where patch decisions must balance cybersecurity urgency with production stability, regulatory compliance, and operational continuity. However, the study also acknowledges significant implementation challenges, including: ·                  hallucination risk ·                  incomplete operational context ·                  governance constraints ·                  integration complexity ·                  model drift ·                  trust barriers ·                  AI security risks Therefore, while full autonomy may not immediately be appropriate for all enterprise environments, a phased human-in-the-loop to human-on-the-loop autonomy model represents a practical adoption path. Future research should focus on empirical validation, reinforcement learning optimization, digital twin simulation, predictive remediation intelligence, and self-healing infrastructure integration. In conclusion, autonomous multi-agent AI represents a promising next-generation paradigm for enterprise patch management modernization and may become a foundational capability in future intelligent enterprise operations.

Use IEEE numbering format in final submission. Core AI / Multi-Agent References [1] Russell, S., & Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Education. [2] Wooldridge, M. An Introduction to MultiAgent Systems. Wiley. [3] OpenAI Research Publications on Large Language Models and autonomous AI systems. [4] Microsoft AutoGen Research Documentation. [5] CrewAI Framework Documentation.   Cybersecurity / Vulnerability References [6] National Institute of Standards and Technology (NIST). Guide to Enterprise Patch Management Technologies. [7] NIST National Vulnerability Database (NVD). [8] OWASP Foundation. Vulnerability Management Guide. [9] MITRE CVE Program Documentation. DevOps / DevSecOps References [10] Kim, G., Humble, J., Debois, P., Willis, J. The DevOps Handbook. [11] Humble, J., Farley, D. Continuous Delivery. [12] DevSecOps operational framework publications. AIOps / Enterprise Operations [13] Gartner Research on AIOps and intelligent infrastructure automation. [14] Studies on AI-driven enterprise incident automation. [15] Enterprise patch orchestration platform whitepapers.