Toward a Unified Interoperability Framework for Autonomous AI Agent Ecosystems: MCP, A2A, ACP, and ANP

The proliferation of large language model (LLM)-powered autonomous agents across enterprise software delivery, cloud operations, and knowledge management has created an urgent systems-level challenge: agents built by different vendors, using different frameworks, cannot discover, authenticate, or collaborate with one another without expensive bespoke integration. Four emerging open protocols—Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), Agent Communication Protocol (ACP), and Agent Network Protocol (ANP)—address different layers of this interoperability gap, yet no formal comparative analysis or unified architectural framework has appeared in the peer-reviewed literature. This paper fills that gap. We provide the first systematic multi-dimensional comparison of all four protocols across interaction mode, discovery mechanism, transport layer, security model, and enterprise-readiness. We introduce a novel Three-Layer Agentic Stack (TLAS) that maps each protocol to its architectural role, propose a formal threat model covering protocol-specific attack surfaces including cross-server shadowing, credential relay, and agent impersonation, and define a phased adoption roadmap validated against real deployment patterns observed in financial-sector and cloud-native environments. Our analysis demonstrates that MCP and A2A are complementary rather than competing, that ACP and ANP serve distinct ecosystem niches, and that a unified TLAS approach reduces integration complexity by an estimated 60–70% compared to ad-hoc solutions. We conclude with open research questions on semantic conflict resolution, decentralized identity binding, and standardized evaluation benchmarks.