MPAC: A Multi-Principal Agent Coordination Protocol for Interoperable Multi-Agent Collaboration

2026-04-10

AI Summary

This work addresses the limitations of existing AI agent protocols—such as MCP and A2A—which support only single-agent control and struggle to coordinate multi-agent collaboration under shared state, often resorting to unstructured chat or silent overwrites. To overcome this, the paper introduces MPAC, the first standardized coordination protocol for multi-agent collaboration. MPAC structures coordination through a five-layer architecture encompassing conversation, intent, action, conflict, and governance, treating conflict as a first-class object. It enables intent pre-declaration, structured conflict representation, and pluggable human arbitration. The protocol specifies 21 message types, three state machines, Lamport-clock-based causal watermarking, optimistic concurrency control, and multiple security configurations, with reference implementations in Python and TypeScript. Evaluated on a three-agent code review benchmark, MPAC reduces coordination overhead by 95% and achieves a 4.8× end-to-end speedup over a human-sequential baseline, without increasing per-agent decision latency.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The AI agent ecosystem has converged on two protocols: the Model Context Protocol (MCP) for tool invocation and Agent-to-Agent (A2A) for single-principal task delegation. Both assume a single controlling principal, meaning one person or organization that owns every agent. When independent principals'agents must coordinate over shared state, such as engineers'coding agents editing the same repository, family members planning a shared trip, or agents from different organizations negotiating a joint decision, neither protocol applies, and coordination collapses to ad-hoc chat, manual merging, or silent overwrites. We present MPAC (Multi-Principal Agent Coordination Protocol), an application-layer protocol that fills this gap with explicit coordination semantics across five layers: Session, Intent, Operation, Conflict, and Governance. MPAC makes intent declaration a precondition for action, represents conflicts as first-class structured objects, and supports human-in-the-loop arbitration through a pluggable governance layer. The specification defines 21 message types, three state machines with normative transition tables, Lamport-clock causal watermarking, two execution models, three security profiles, and optimistic concurrency control on shared state. We release two interoperable reference implementations in Python and TypeScript with 223 tests, a JSON Schema suite, and seven live multi-agent demos. A controlled three-agent code review benchmark shows a 95 percent reduction in coordination overhead and a 4.8 times wall-clock speedup versus a serialized human-mediated baseline, with per-agent decision time preserved. The speedup comes from eliminating coordination waits, not compressing model calls. Specification, implementations, and demos are open source.

Problem

• multi-principal coordination
• interoperable multi-agent systems
• shared state collaboration
• agent interoperability
• coordination protocol

Innovation

• Multi-Principal Coordination
• Agent Interoperability
• Optimistic Concurrency Control
• Intent-Based Coordination
• Structured Conflict Resolution