Agentic AI Frameworks for Enterprise Risk Advisory: Overcoming Legacy Integration and Governance Challenges in Production Deployments

Enterprise risk management is undergoing a structural transformation driven by the emergence of production-grade agentic artificial intelligence. Unlike earlier generations of AI-assisted tooling, agentic systems operate with goal-directed autonomy, decomposing complex risk workflows into coordinated subtasks executed across specialised agents. This paper presents the Agentic Enterprise Risk Advisory (AERA) framework, a multi-agent prototype architecture designed to address three foundational production challenges: the integration of intelligent agents with heterogeneous legacy enterprise data pipelines; the enforcement of governance layers that produce auditable, explainable, and cost-bounded decisions; and the orchestration of Retrieval-Augmented Generation (RAG) pipelines with robust failover mechanisms in high-stakes risk scenarios. The AERA framework deploys six specialised agents: Orchestrator, Document Forensics, Compliance Audit, Legacy Bridge, Risk Scoring, and Governance — interconnected via an asynchronous message bus. Experimental evaluation across document forgery detection and regulatory compliance auditing workflows demonstrates F1 improvements from 0.71 to 0.94 for forgery detection, a reduction in false-positive compliance flags from 18.4% to 6.1%, and a decrease in mean audit report generation time from 14.2 hours to 3.8 hours. Governance audit coverage increased from 42% to 97% following deployment of the immutable decision ledger. These results confirm that thoughtfully engineered agentic architectures can deliver meaningful operational value in regulated enterprise contexts, provided that governance and integration challenges are treated as first-class architectural concerns rather than afterthoughts.