Enterprise AI Control Plane : A Governance Infrastructure for Autonomous AI Decision Systems

Autonomous AI agents are rapidly becoming operational actors inside enterprise systems.Modern agentic architectures built on large language models (LLMs) can retrieve enterprise data, invoke APIs, coordinate multi-step work ows, and execute operational decisionsacross nancial services, healthcare, customer operations, and enterprise IT without pausing for human con rmation at each step. These interactions generate a new and previouslyungoverned category of system activity: AI decision tra c the stream of prompts, contextual retrieval events, model inferences, reasoning outputs, and tool invocations that togetherproduce operational actions.Despite the rapid deployment of such systems, enterprises lack infrastructure capableof governing this decision layer. Traditional enterprise platforms including API gateways,identity systems, and observability tools were designed to manage deterministic softwareservices and cannot interpret or regulate the probabilistic reasoning processes that driveautonomous AI behaviour.This paper introduces the Enterprise AI Control Plane, a new category of enterpriseinfrastructure designed to observe, govern, and enforce policy over AI decision tra c inreal time. We present Eaigins (Enterprise Agentic Intelligence Governance InfrastructureService) as a reference implementation that intercepts AI interactions, evaluates governancepolicies, assesses operational risk, and records decision provenance through a ve-stage sequential pipeline: Runtime Interceptor → AI Agent Firewall → Policy Enforcement Gateway→Risk Scoring Engine → Decision Provenance Engine.We establish why pre-execution governance is structurally non-negotiable, demonstratethe Control Plane pattern as a proven extension of enterprise computing history, and alignthe architecture with the EU AI Act European Parliament & Council, 2024, NIST AIRisk Management Framework NIST, 2023, and ISO/IEC 42001 ISO/IEC, 2023. A velevel governance maturity model is presented to guide enterprise adoption. The architectureaddresses the OWASP Top 10 for LLM Applications OWASP Foundation, 2025, providingconcrete mitigations for prompt injection, data ex ltration, and unauthorised tool invocationthreats. Empirical evidence from thirteen validated deployment scenarios demonstrates thatthe Control Plane pattern blocks critical failure modes without impeding legitimate agentoperations