Governing Agentic AI in Enterprise Workflows: A Bounded-Autonomy Framework for Delegated Authority and Controlled Execution

Agentic AI can interpret information, plan, make workflow decisions, and use enterprise tools. Yet technical capability does not establish authoritative meaning, legitimate process state, organisational permission, or accountable execution. The challenge is to preserve adaptability while ensuring that consequential actions remain governed. This article develops a domain-independent conceptual framework for governed agentic AI in enterprise workflows based on bounded autonomy, delegated authority, and controlled execution. A consequential action or workflow decision selected by an agent is treated as a proposed action. It may change enterprise state only after independent controls confirm semantic validity, procedural admissibility, policy compliance, and delegated authority. Actions that pass these controls and remain within a task envelope may proceed automatically through controlled enterprise tools. Those exceeding thresholds for consequence, irreversibility, uncertainty, data sensitivity, value, or organisational policy are escalated to an accountable human. Human oversight is therefore risk-proportionate rather than required for every action. The framework integrates enterprise ontologies, governed knowledge graphs, Business Process Model and Notation (BPMN) orchestration, policy and decision services, controlled tool execution, and provenance within explicit responsibility and authority boundaries. A review-informed design-science process synthesises evidence into five connected control gaps and derives ten design requirements, operationalised through task envelopes, capability and authority relations, lifecycle states, exception paths, and conformance criteria. An illustrative online-shopping order exception demonstrates the control logic, while a control-loop walkthrough and ten failure and adversarial conditions trace requirements-to-control mappings, responsibility separation, and defined recovery paths. The framework provides a systematic basis for governing agentic AI as an adaptable enterprise participant. It supports risk-proportionate autonomy, auditability, accountability, and regulatory evidence. The analytical walkthrough supports conceptual coherence and design plausibility but does not establish deployed effectiveness or legal compliance.