MCP Is a Great Start — But Multi-Agent Production Needs More

The Model Context Protocol (MCP) has revolutionized how AI connects to tools. However, connecting agents to each other poses a significant challenge. A recent article by @gramli titled WebMCP Attack Lab: Can an E-Shop Hijack Your AI Agent? highlighted the issues I've encountered in production. This piece expands on what makes MCP powerful and delves into the complexities of multi-agent production systems.

The core issue in multi-agent discussions is state coordination. While frameworks like LangChain, AutoGen, and CrewAI excel at individual agent capabilities, they falter when agents need to share state. For instance, when two agents access shared context simultaneously, the first agent writes new context (version 2), while the second agent writes context based on version 1, overwriting the first agent's work. This silent loss of data is the primary failure mode in multi-agent production systems.

To address this, I developed Network-AI, an open-source coordination layer that sits between agents and shared state. This layer ensures atomic state updates through a propose → validate → commit cycle. Instead of direct writes that can cause conflicts, state mutations go through this rigorous process. Network-AI supports 14 frameworks, including MCP, AutoGen, CrewAI, and more, while providing token budget control, permission gating, and a full audit trail.

MCP excels at connecting agents to tools, while Network-AI handles the coordination between agents. Together, they form a comprehensive production stack for multi-agent systems. You can explore Network-AI on GitHub (MIT license) and join the Discord community for discussions. If you're running MCP agents in production and facing coordination challenges, share your experiences in the comments.