Trace-Based Governance for Agentic AI: An MCP-Based Framework for Observability, Human Oversight, and Accountability

Abstract

Agentic AI systems, LLM-based agents that autonomously plan multi-step tasks and execute them via tool calls, are increasingly deployed in organizations, yet systematic instruments to operationalize governance, transparency, and accountability for agentic tool usage remain lacking. This paper presents the conceptual design of a trace-based governance framework that operates without access to internal model reasoning (Chain-of-Thought) and instead leverages observable execution evidence. The core artifact is an MCP (Model Context Protocol) Governance Gateway, a platform-agnostic integration layer positioned between AI host applications and organizational tools. The framework addresses three governance dimensions: (1) transparency as observability through structured evidence capture of tool calls, data provenance, and human approvals; (2) human oversight through risk-based policy mechanisms including approval gating and capability boundaries; and (3) accountability through auditable logs with tamper-evident integrity mechanisms. We formalize governance requirements derived from the EU AI Act and EU HLEG Trustworthy AI guidelines, propose an Agent Action & Evidence Graph as a formal evidence model, and outline a three-stage evaluation strategy. The framework contributes a novel approach to operationalizing regulatory AI governance requirements for heterogeneous agentic tool ecosystems.