Agent Mesh Networks: Collaborative Intelligence via MCP

[gogakoreli]

Pre-submission Checklist

• I have verified this would not be more appropriate as a feature request in a specific repository
• I have searched existing discussions to avoid duplicates

Your Idea

The Problem

Current MCP enables AI agents to use tools, but complex problems require multiple AI perspectives working together. Today's
agents remain isolated - a security expert can't directly collaborate with a database expert to solve comprehensive
problems requiring multi-disciplinary analysis.

The Solution: Agent Mesh Networks

Enable MCP agents to discover and collaborate with other specialized agents, forming dynamic networks of expertise:

               You
                ↕
       Main Assistant (Orchestrator)
                ↕
   Security Expert ↔ Database Expert ↔ Code Expert
        ↕               ↕               ↕
   Network Expert ↔ Performance Expert ↔ UI Expert

Example workflow:

You: "Help me optimize this database query"
→ Main Assistant identifies need for database expertise
→ Database Expert analyzes performance issues
→ Security Expert (via mesh) flags SQL injection risks  
→ Performance Expert suggests indexing improvements
→ All experts collaborate through the mesh
→ Main Assistant synthesizes recommendations back to you

Your LLM assistant becomes an orchestrator coordinating with specialized MCP agent experts who can collaborate with each other and communicate back through your assistant.

How It Works

Building on existing MCP capabilities:

• Agent Discovery: Extend resources/list to advertise agent capabilities and specializations

• Agent Communication: Leverage sampling/createMessage for agent-to-agent reasoning:
• Agent A samples from Agent B's specialized LLM
• Model preferences route to appropriate specialist agents
• Human-in-the-loop controls maintain security
• Context flows through structured prompts and responses

• Context Sharing: Use enhanced session management for shared state across agent interactions

• Intelligent Routing: Dynamic team formation based on problem requirements and agent capabilities

Trust & Security

Leverages existing MCP security model: Agents are specialized MCP servers that clients explicitly register and approve.
Agent-to-agent communication inherits all current MCP protections:

• Client controls which agent servers to connect to (same as MCP server registration today)
• Human-in-the-loop approval for agent sampling requests (existing sampling security)
• Explicit trust boundaries - no agent can communicate without client permission
• Familiar security patterns - developers use the same trust model they already know

This approach requires no new security infrastructure - agent mesh networks inherit the robust trust model MCP already
provides.

Who Benefits

• Enterprise teams needing multi-disciplinary AI analysis (security + database + code review)
• Complex domains requiring coordinated expertise (healthcare, finance, legal)
• AI researchers building sophisticated collaborative systems
• Any organization solving problems that span multiple knowledge domains

Implementation Approach

Phase 1: Agent discovery and basic communication using existing MCP primitives (sampling, resources)
Phase 2: Enhanced context sharing and persistent collaboration sessions
Phase 3: True mesh networks with emergent collective intelligence

This transforms MCP from individual tool access into collaborative intelligence networks where specialized AI agents work
together like expert human teams.

Scope

• Protocol Specification
• SDK Features
• Documentation
• Developer Experience
• Other