Mesh Multi‐Context Protocol: An Adaptive Agentic AI With Mesh Concept for A Complex Ecosystem

The deployment of Agentic AI systems‐autonomous agents powered by large language models (LLMs) ‐ has exposed critical vulnerabilities in multi‐domain enterprise environments, with recent incidents including autonomous production database deletions highlighting fundamental architectural limitations. While frameworks such as the Multi‐Context Protocol (MCP) enable LLMs to access multiple data sources, they lack domain specialization, fault isolation, and federated governance mechanisms necessary for safe enterprise‐scale deployment across heterogeneous legacy systems. This paper introduces the Mesh Multi‐Context Protocol (MMP), a novel framework synthesizing Agentic AI capabilities with mesh topology principles to address these limitations. MMP contributes five key innovations: dynamic node management, domain‐specialized nodes, legacy system integration, lightweight operation and federated multi‐node topology. By treating each domain as an autonomous node, MMP enables safe cross‐organizational coordination while maintaining data sovereignty and preventing systemic failures. Unlike existing approaches requiring large foundation models and centralized orchestration, MMP supports lightweight domain‐specific language models with incremental adoption pathways suitable for air‐gapped, highly regulated environments including healthcare, government, and financial services. Through comprehensive architectural analysis, we demonstrate how MMP addresses fundamental challenges of deploying autonomous AI agents in complex ecosystems where privacy preservation, regulatory compliance, and operational resilience are paramount, establishing the theoretical foundation for empirical validation in real‐world system‐of‐systems scenarios.