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Agent Communication Protocol (ACP)

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Agent Communication Protocol (ACP): The Open Standard for AI Agent Interoperability

Open-sourced by IBM Research and governed under the Linux Foundation, the Agent Communication Protocol (ACP) is designed to break silos in today’s fragmented agent ecosystem.

Modern AI systems often rely on specialized modular agents (retrievers, reasoners, tool-users, classifiers, etc.). But these agents are typically locked into specific frameworks, leading to duplicated work, isolated systems, and one-off integrations that don’t scale.

ACP solves this by providing a lightweight, HTTP-native, open protocol for agent-to-agent (and agent-to-human/application) communication. Think of it as the “HTTP for AI agents.”

Why ACP?

  • Fragmentation challenge: Different frameworks (LangChain, AutoGen, CrewAI, BeeAI, etc.) don’t interoperate well.
  • Integration pain: Each agent often needs custom glue code.
  • Innovation bottleneck: Harder to reuse, extend, or collaborate across teams and companies.

ACP standardizes how agents talk to each other while letting developers decide how they’re built.

Repo & Docs → Agent Communication Protocol GitHub

Core Features of ACP

  • REST-Based Design → Built on HTTP, exposing clear endpoints for sending, receiving, and routing agent messages. Works with cURL, Postman, or any HTTP client.
  • SDK-Optional → Use ACP with just HTTP requests, or streamline with SDKs for Python & TypeScript.
  • Multimodal Messaging → Support for text, code, embeddings, files, and images for complex workflows (LLMs + vision + tools).
  • Async by Default → Handles long-running tasks natively, with synchronous + streaming (SSE) support for low-latency apps.
  • Discovery → Metadata-driven discovery of agents (even offline).
  • Shared State → Long-running workflows can maintain and share context across agents.

Reference: IBM Research Blog

Governance & BeeAI Ecosystem

ACP is developed under open governance (Linux Foundation).
Its primary implementation is via the BeeAI Framework, which supports:

  • Building ACP-compliant agents in Python or TypeScript
  • Discovery, sharing, and running of agents
  • Deployment infrastructure via the BeeAI Platform

This ensures ACP isn’t just a spec—it’s a working ecosystem for production use.

️ How ACP Works

  1. Build & Wrap your Agent → Run it as an ACP server exposing REST endpoints.
  2. ACP Client Requests → The client routes requests to the appropriate agent(s).
  3. Client-as-Agent → The ACP Client can itself act as an agent, forwarding tasks intelligently.

This allows seamless workflows, where agents collaborate in real time across modalities, systems, or even organizations.

Detailed: IBM Think Overview

Use Cases

  • Dynamic Updating → Swap or upgrade agents without breaking integrations. (Repo Docs)
  • Specialized Agents Working as a Team → A research agent, a visualization agent, and a finance agent collaborating like a human project team.
  • Cross-System Workflows → Customer support agent ↔ inventory agent ↔ HR agent. Each system stays modular but interoperable.
  • Inter-Organizational Collaboration → Agents across different companies securely collaborating (e.g., supply chain optimization).

Read: ADaSci Practitioner’s Guide

ACP vs MCP vs A2A

MCP (Model Context Protocol, Anthropic)

  • Focus: Tool/context access for single agent
  • Communication: JSON-RPC + streaming
  • Governance: Proprietary (Anthropic)
  • Best For: Enriching agent cognition with external tools

ACP (Agent Communication Protocol, IBM/Linux Foundation)

  • Focus: Agent-to-agent interoperability
  • Communication: RESTful, HTTP-native
  • Governance: Open (Linux Foundation / BeeAI)
  • Best For: Local-first, low-latency, edge/team workflows

A2A (Agent-to-Agent, Google)

  • Focus: Federated inter-agent cloud cooperation
  • Communication: Flexible, natural HTTP/JSON
  • Governance: Proprietary (Google ecosystem)
  • Best For: Cross-vendor, cloud-native collaboration

Analysis: Everest Group – The Rise of Agent Protocols

Resources & Talks

Final Thoughts

ACP is to AI agents what HTTP was to the web:
a universal, open standard that enables communication, discovery, and collaboration—across frameworks, teams, and even companies.

By adopting ACP, we move closer to a world where AI agents aren’t isolated bots, but networked collaborators.

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Really appreciate the post Do you think ACP could become the default standard for agent interoperability, or will proprietary protocols like MCP and A2A continue to dominate in certain sectors?

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Sorry for the late response, and really appreciate your thoughtful comment .

To give a quick comparison:

  • MCP (Model Context Protocol) – Designed for model ↔ tool interoperability. It standardizes how models access external context like memory, databases, or APIs. Great for structured tool access, but it’s not really built for multi-agent collaboration.
  • A2A (Agent-to-Agent protocols) – Typically proprietary or vendor-specific ways agents communicate with each other. They work well within closed ecosystems, but the lack of standardization limits cross-platform interoperability.
  • ACP (Agent Communication Protocol) – Aims to be an open standard for agent ↔ agent communication, covering message exchange, negotiation, delegation, and coordination. It’s more general-purpose than MCP and designed for multi-agent ecosystems.

Pros & Cons:

  • MCP → strong tool integration, structured context handling. but not designed for agent-to-agent workflows.

  • A2A → optimized within one vendor’s ecosystem. creates silos, but poor cross-vendor interoperability.

  • ACP → focuses on interoperability, cross-vendor collaboration, and flexible agent ecosystems. but still early, adoption is limited, and there’s a risk of fragmentation if not widely accepted.

Personally, I think ACP has the potential to emerge as a baseline standard for agent interoperability as ecosystems grow. But I also see MCP and proprietary A2A approaches continuing in specialized or vendor-controlled domains where deep integration matters more than openness.

Likely, we’ll end up with ACP as the “lingua franca” for agents, while MCP/A2A coexist for context-sharing and vertical-specific use cases.

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