The Agentic Enterprise Capability Framework (AECF): A Governance-First Architecture for Scalable AI Agent Deployments
TL;DR
The study contributes an integrated architectural model, propositional formalization, and validation agenda for governed enterprise AI agent deployments, and introduces the co-evolution constraint: technical capability layers cannot mature independently of governance capacity.
Abstract
Enterprise AI adoption has reached a structural inflection point: while a majority of organizations have deployed generative AI, few have established mature governance models for autonomous agents. This disparity reflects a fundamental architectural gap: Multi-Agent Systems, Enterprise Architecture, AI agent deployment, and software architecture have approached agent coordination from separate disciplinary perspectives, with no single framework integrating persistent memory, semantic interoperability, orchestration, human oversight, and normative enforcement. Following Design Science Research, this article develops the Agentic Enterprise Capability Framework (AECF), a five-layer architecture structured around Context Persistence (CPL), Semantic Interoperability (SIL), Hybrid Orchestration (HOL), Human Governance Interface (HGI), and Governance Envelope (GEL). The framework introduces the co-evolution constraint: technical capability layers cannot mature independently of governance capacity. This constraint is operationalized through Context-Enriched Pre-Execution Validation (CEPEV), which grounds compliance checks in operational memory. Five architectural propositions formalize inter-layer dependencies (P1), scalability boundaries (P2), governance effectiveness (P3), performance accumulation (P4), and a governance scaling law (P5). The study contributes an integrated architectural model, propositional formalization, and validation agenda for governed enterprise AI agent deployments.