Autonomous Software License Intelligence: A Governance-Aware Agentic AI Framework for Enterprise Software Asset Management

Abstract

Enterprise software licensing practices are more complex when organizations mix on-premises and cloud-based applications and multiple Software as a Service (SaaS) subscriptions from multiple publishers and internal departments․ Software Asset Management (SAM) best practices have traditionally relied on regular discovery‚ manual entitlement reconciliation‚ exception handling‚ and software management intervention at contract renewal․ We propose the Autonomous Software License Intelligence (ASLI) framework: a governance-aware agentic AI framework for continuous discovery‚ use‚ compliance‚ optimization‚ and execution of software licenses․ Anchored in Design Science Research (DSR)‚ ASLI presents a prescriptive five-layer artifact including the layers of discovery and ingestion‚ license intelligence‚ agentic orchestration‚ compliance and risk‚ and executive reporting․ ASLI employs retrieval-augmented generation techniques for contract and policy reasoning‚ multi-agent orchestration for complex disposition workflows‚ and a confidence-scored governance mechanism for escalations to humans‚ human approval/signoff‚ or autonomous execution․ In order to quantify the artifact‚ five operational metrics have been defined: Software Utilization Variance Index (SVI)‚ License Inefficiency Score (LIS)‚ Estimated License Optimization Savings (ELOS)‚ Autonomous Confidence Score (ACS)‚ and License Drift and Fragmentation Index (LDFI)․ The paper also shows how third-party benchmarks of Flexera‚ Zylo‚ the FinOps Foundation‚ and Gartner can be used for index calibration‚ but does not report any production deployment experience with this approach․ Thus‚ the paper's contribution to AI-enabled SAM is the evaluated architectural artifact and the measurement model․ The framework additionally distinguishes between deterministic automation and agentic reasoning‚ and conditions under which AI systems may be safely shifted from recommendation to supervised or autonomous execution․