• Vendor Agnostic • Production Ready

The Control Plane for
Multi-Agent AI Systems

When you deploy multiple AI agents in production, they corrupt data, bypass policies, and operate in silos.
ProtoMesh provides the coordination infrastructure you need.

Building coordination infrastructure for autonomous AI agents

Every Multi-Agent System Faces These Problems

Resource Conflicts

Multiple agents simultaneously access and modify shared resources, causing data corruption and race conditions.

Zero Governance

Agents operate without policy enforcement. Junior agents approve high-value transactions, bypass access controls.

Framework Silos

Agents built on different frameworks (LangGraph, CrewAI, custom) cannot coordinate or communicate.

See ProtoMesh in Action

Two independent agents (Gemini + Groq) attempt to modify the same customer balance simultaneously. Without ProtoMesh, data is lost. With ProtoMesh, access is coordinated.

How ProtoMesh Works

1

Agents Request Locks

Before accessing shared resources, agents request distributed locks from ProtoMesh's control plane.

2

Priority-Based Queuing

ProtoMesh grants locks by priority, not first-come-first-served. Critical agents jump the queue.

3

Policy Enforcement

Before granting access, ProtoMesh checks policies: spending limits, access controls, approval workflows.

4

Automatic Handoff

When an agent releases a lock, ProtoMesh automatically grants it to the next highest-priority agent.

# Agent connects to ProtoMesh
pm = ProtoMeshClient(url)
# Request coordinated access
lock = await pm.acquire_lock(
resource="customer",
id="123",
priority=8
)
# Safely modify resource
customer.balance += 200
# Release lock
await pm.release_lock(lock)

Frequently Asked Questions