A Unified Protocol Architecture for Agentic Interoperability: Quantitative Evaluation of Standardized Context Exchange in the N + M Economy

The rapid evolution of autonomous artificial intelligence systems has catalyzed the emergence of the “Agentic Economy,” a paradigm where decentralized intelligent agents execute complex workflows through heterogeneous tool environments. However, the scalability of this economy is fundamentally constrained by the N × M integration bottleneck, wherein N models require bespoke, brittle connectors for M data sources. This paper presents a comprehensive theoretical and quantitative evaluation of Universal Agentic Interoperability Protocols (UAIP), such as the Model Context Protocol (MCP). We formalize a Multi-Attribute Utility Analysis (MAUA) framework to assess the transition from point-to-point (P2P) RESTful architectures
to standardized, stateful RPC-based hubs. Our analysis incorporates high-fidelity metrics for Architectural Entropy (Sa), Technical Debt Decay (δTD), and Protocol Efficiency (η). Through a large-scale enterprise simulation involving 100 agents and 500 tools, we demonstrate that UAIP adoption reduces total system configuration entropy by 84% and collapses maintenance overhead by an order of magnitude. The results provide a rigorous basis for the standardization of context exchange in future multi-agent ecosystems, ensuring that the next generation of AI infrastructure remains scalable, secure, and vendor-neutral.

• Machine intelligence

• Networking