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

• URL: http://arxiv.org/abs/2604.09744v1
• Date: Fri, 10 Apr 2026 01:12:05 GMT
• Title: MPAC: A Multi-Principal Agent Coordination Protocol for Interoperable Multi-Agent Collaboration
• Abstract summary: MPAC is an application-layer protocol that fills this gap with explicit coordination semantics across five layers.<n> specification defines 21 message types, three state machines with normative transition tables, Lamport-clock causal watermarking, two execution models, three security profiles, and optimistic control on shared state.<n>A controlled three-agent code review benchmark shows a 95 percent reduction in coordination overhead and a 4.8 times wall-clock speedup.
• Score: 2.643905034529239
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Technical Taxonomy of LLM Agent Communication Protocols [60.76747983053368]
  This study develops a technical taxonomy to classify and analyze large language models (LLMs) agent communication protocols.<n>The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.
  arXiv  Detail & Related papers  (2026-06-17T14:45:20Z)
• Collaborative Human-Agent Protocol (CHAP) [0.18352113484137622]
  This paper presents CHAP, the Collaborative Human-Agent Protocol.<n>Under CHAP, the override that used to vanish into a chat thread becomes a structured event carrying a diff, a rationale, and a content hash.<n>The protocol achieves this through a small Core (workspaces, participants, tasks, artefacts, and an append-only evidence log) together with composable profiles that add review, modes, routing, deliberation, handoff, identity, signatures, and transparency-backed audit as deployments require them.
  arXiv  Detail & Related papers  (2026-06-08T17:11:42Z)
• Benchmarking Open-Ended Multi-Agent Coordination in Language Agents [48.1363632826625]
  Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world.<n>We introduce $alem$, a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics.
  arXiv  Detail & Related papers  (2026-06-06T21:13:43Z)
• Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs [52.26652574704317]
  Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes.<n>We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems.
  arXiv  Detail & Related papers  (2026-05-07T14:19:17Z)
• Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems [0.9494375075678443]
  LLM agents increasingly collaborate on tasks spanning days or weeks.<n>This requires agents to share, evaluate, and combine each other's cognitive state in real time across sessions.<n>We call this cross-session agent-to-agent cognitive collaboration, distinct from parallel agent execution.
  arXiv  Detail & Related papers  (2026-04-21T15:00:25Z)
• OrgAgent: Organize Your Multi-Agent System like a Company [75.47076168155817]
  We introduce OrgAgent, a company-style hierarchical multi-agent framework.<n>We find that multi-agent systems organized in a company-style hierarchy generally outperform other organizational structures.
  arXiv  Detail & Related papers  (2026-04-01T15:21:14Z)
• AgentRFC: Security Design Principles and Conformance Testing for Agent Protocols [2.1352949241716352]
  Agent protocols enable autonomous agents to discover capabilities, delegate tasks, and compose services across trust boundaries.<n>Despite massive deployment, no systematic security framework for these protocols exists.<n>We present three contributions: the Agent Protocol Stack, the Agent-Agnostic Security Model, and AgentConform.
  arXiv  Detail & Related papers  (2026-03-25T00:25:02Z)
• Bridging Protocol and Production: Design Patterns for Deploying AI Agents with Model Context Protocol [0.0]
  The Model Context Protocol (MCP) standardizes how AI agents discover and invoke external tools.<n>Three protocol-level primitives remain missing: identity propagation, adaptive tool budgeting, and structured error semantics.<n>This paper identifies these gaps through field lessons from an enterprise deployment of an AI agent platform integrated with a major cloud provider's MCP servers.
  arXiv  Detail & Related papers  (2026-03-12T17:42:24Z)
• AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction [77.62279834617475]
  We propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.<n>Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection, which enables each agent to selectively access relevant information from any previous step.
  arXiv  Detail & Related papers  (2025-06-21T18:34:43Z)
• Agent-as-a-Service based on Agent Network [9.5094423572869]
  We propose Agent-as-a-Service based on Agent Network (A-AN), a service-oriented paradigm grounded in the Role-Goal-Process-Service (RGPS) standard.<n>A-AN unifies the entire agent lifecycle, including construction, integration, interoperability, and networked collaboration.<n>We release a dataset containing 10,000 long-horizon multi-agent to facilitate future research on long-chain collaboration in MAS.
  arXiv  Detail & Related papers  (2025-05-13T11:15:19Z)
• A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP) [0.8463972278020965]
  This survey examines four emerging agent communication protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)
  arXiv  Detail & Related papers  (2025-05-04T22:18:27Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC [98.82146219495792]
  In this paper, we propose a hierarchical agent framework named PC-Agent.<n>From the perception perspective, we devise an Active Perception Module (APM) to overcome the inadequate abilities of current MLLMs in perceiving screenshot content.<n>From the decision-making perspective, to handle complex user instructions and interdependent subtasks more effectively, we propose a hierarchical multi-agent collaboration architecture.
  arXiv  Detail & Related papers  (2025-02-20T05:41:55Z)
• Multi-agent Deep Covering Skill Discovery [50.812414209206054]
  We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space. Also, we propose a novel framework to adopt the multi-agent options in the MARL process. We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
  arXiv  Detail & Related papers  (2022-10-07T00:40:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.