Architecting Agentic Artificial Intelligence Systems for Autonomous Enterprise Operations: A Framework for Intelligent Incident Resolution and Operational Decision-Making

Abstract

Enterprise IT operations face a widening mismatch between incident volume and variability on one side, and the rule-based automation built to manage them on the other. Scripted runbooks and static alerting thresholds work well for narrow, predictable failures, but they degrade quickly as environments scale in service count, dependency depth, and telemetry volume. This paper proposes an architectural framework for agentic artificial intelligence systems aimed at that gap in enterprise incident resolution and operational decision-making. We argue that agentic AI is a genuinely different paradigm from prior automation approaches, built on planning, tool use, and bounded autonomy rather than fixed condition-action scripting. The framework organizes agentic operations into four layers: specialized agents mapped to discrete operational functions (triage, knowledge retrieval, root-cause synthesis, and remediation); an orchestration layer that coordinates these agents across a live incident; a retrieval-augmented generation (RAG) layer that grounds agent outputs in verifiable enterprise knowledge rather than unconstrained model generation; and a governance layer that treats confidence-based escalation, audit trails, and human oversight as structural design requirements rather than afterthoughts. We position this framework against existing work on AIOps, root-cause analysis, and multi-agent orchestration, and we frame our contribution as a continuation of that work rather than an unprecedented capability. Separately from that literature synthesis, we report a set of practitioner-observed outcomes drawn from direct exposure to agentic automation deployments in enterprise settings. These figures are presented explicitly as practitioner observations, not as results from a controlled or peer-reviewed study, and they are not offered as confirmatory evidence for the architecture itself. The paper closes with an honest look at the limits of a single-practitioner evidentiary base and names open problems in governance specification and confidence calibration that the current literature has not resolved.