A four-step framework for moving agentic AI systems from proof-of-concept to production-grade enterprise deployments. The framework covers: (1) a foundation layer using RAG, operational data, and agent memory to reduce hallucinations; (2) a verification layer with Agent Confidence Score (ACS) and Business Risk Score (BRS); (3) a governance layer using an Agent Decision Score formula (ADS = ACS × (1 - BRS)) with a traffic-light routing system for human-in-the-loop escalation; and (4) an outcomes layer with executive dashboards tracking ROI, cost-per-task, and systemic risk. A customer refund workflow is used throughout as a concrete example. MongoDB is positioned as the unified data platform for agent memory, vector search, and execution traces.
