Agent Communication Protocol (ACP)
Agent Communication Protocol (ACP)
https://www.ibm.com/think/topics/agent-communication-protocol
Agent Communication Protocol (ACP) is an open standard for agent-to-agent communication. With this protocol, we can transform our current landscape of siloed agents into interoperable agentic systems with easier integration and collaboration.
With ACP, originally introduced by IBM’s BeeAI, AI agents can collaborate freely across teams, frameworks, technologies and organizations. It’s a universal protocol that transforms the fragmented landscape of today’s AI agents into interconnected teammates and this open standard unlocks new levels of interoperability, reuse and scale. As the next step following the Model Context Protocol (MCP), an open standard for tool and data access, ACP defines how agents operate and communicate.1
For context, an AI agent
is a system or program that is capable of autonomously performing tasks
on behalf of a user or another system. It performs them by designing
its workflow and by using available tools. Multi-agent systems consist of multiple AI agents working collectively to perform tasks on behalf of a user or another system.
As an AI agent communication standard with open governance, ACP allows artificial intelligence agents to communicate across different frameworks and technology stacks. From taking in user queries in the form of natural language to performing a series of actions, AI agents perform better when provided with communication protocols. These protocols relay this information between tools, other agents and ultimately, to the user.
AI agent communication refers to how artificial intelligence agents interact with each other, humans or external systems to exchange information, make decisions and complete tasks. This communication is especially important in multi-agent systems, where multiple AI agents collaborate, and in human-AI interaction.
ACP is part of a growing ecosystem, including BeeAI. The following are some key features and you can read more about the core concepts and details in the official documentation.
Example of an ACP client and ACP agents of different frameworks communicating.
- REST-based communication: ACP uses standard HTTP conventions for communication that makes it easy to integrate into production. Whereas MCP relies on the JSON-RPC format that requires much more complex communication methods.
- No SDK required: ACP doesn’t require any specialized libraries. You can interact with intelligent agents by using tools like cURL, Postman or even your browser. For added convenience, there is an SDK available.
- Offline discovery: ACP agents can embed metadata directly into their distribution packages, which enables discovery even when they’re inactive. This supports scale-to-zero environments, where resources are dynamically allocated and might not always be online.
- Async-first, sync supported: ACP is designed with asynchronous communication as the default. This method is ideal for long-running or complex tasks. Synchronous requests are also supported.
Note: ACP enables agent orchestration for any agentic architecture, but it doesn’t manage workflows, deployments or coordination between agents. Instead, it enables orchestration across diverse agents by standardizing how they communicate. IBM Research built BeeAI, an open source system designed to handle agent orchestration, deployment and sharing by using ACP as the communication layer.
Different agent architectures enabled using ACP.
As agentic AI continues to rise, so does the amount of complexity in navigating how to get the best outcome from each independent technology for your use case, without being constrained to a particular vendor. Each framework, platform and toolkit offer unique advantages but integrating them all into one agentic system is challenging.
Today, most agent systems operate in silos. They’re built on
incompatible frameworks, expose custom APIs and endpoints and lack a
shared protocol for communication. Connecting them requires fragile and
nonrepeatable integrations that are expensive to build.
ACP represents a fundamental shift: from a fragmented, ad hoc ecosystem
to an interconnected network of agents—each able to discover, understand
and collaborate with others, regardless of who built them or what stack
they run on. With ACP, developers can harness the collective
intelligence of diverse agents to build more powerful workflows than a
single system can achieve alone.
Despite rapid growth in agent capabilities, real-world integration remains a major bottleneck. Without a shared communication protocol, organizations face several recurring challenges:
- Framework diversity: Organizations typically run hundreds or thousands of agents built by using different frameworks like LangChain, crewAI, AutoGen or custom stacks.
- Custom integration: Without a standard protocol, developers must write custom connectors for every agent interaction.
- Exponential development: With n agents, you potentially need n(n-1)/2 different integration points, which makes large-scale agent ecosystems difficult to maintain.
- Cross-organization considerations: Different security models, authentication systems and data formats complicate integration across companies.
To illustrate the real-world need for agent-to-agent communication, consider two organizations:
- A manufacturing company that uses an autonomous agent to manage production schedules and order fulfillment based on internal inventory and customer demand.
- A logistics provider that runs an agent to offer real-time shipping estimates, carrier availability and route optimization.
A use case example of two agents (manufacturing and logistics) enabled with ACP and communicating with one another across organizations.
Now imagine the manufacturer’s system needs to estimate delivery timelines for a large, custom equipment order to inform a customer quote.
Without ACP: This approach requires building a bespoke integration between the manufacturer’s planning software and the logistics
provider’s APIs.
This means handling authentication, data format mismatches and service
availability manually. These integrations are expensive, brittle and
hard to scale as more partners join.
With ACP: Each
organization wraps its agent with an ACP interface. The manufacturing
agent sends order and destination details to the logistics agent, which
responds with real-time shipping options and ETAs. Both systems perform
agentic collaboration without exposing internals or writing custom
integrations. New logistics partners can be introduced simply by
implementing ACP. The automation that AI agents paired with ACP provide
allows for scalability and streamlining data exchanges.
https://medium.com/@SreePotluri/understanding-the-agent-communication-protocol-acp-and-its-evolution-from-mcp-c28ad30c8ee0
Introduction
The Agent Communication Protocol (ACP) is a developing standard designed to facilitate seamless communication between AI agents. It builds upon the Model Context Protocol (MCP) while addressing certain limitations and introducing enhancements to improve interoperability, efficiency, and scalability. As a Solution Architect, I see ACP as a promising advancement that can streamline AI-driven automation and enhance system integrations. In this post, I’ll break down MCP, how ACP differs, and the key ideas that could make life easier for architects like myself.
https://learn.deeplearning.ai/courses/acp-agent-communication-protocol/information
Disclaimer: Starting September 1, 2025, the ACP team joined forces with Google’s A2A protocol team to develop a unified standard for agent communication. Learn more about the collaboration here. As a result, this course will be available for a limited time and will eventually be replaced by an updated course on the A2A protocol.
Introducing ACP: Agent Communication Protocol, a short course built in partnership with IBM Research’s BeeAI and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM.
Building a multi-agent system with agents shared across teams and organizations can be challenging. You may need to write custom integrations each time a team updates their agent design or changes the agent’s framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing communication between agents. It provides a unified interface through which agents can collaborate regardless of their frameworks, making it easy to replace an agent with a new version without needing to refactor the entire system.
https://github.com/i-am-bee/beeai-framework
BeeAI Framework
BeeAI Framework is a comprehensive toolkit for building intelligent, autonomous agents and multi-agent systems. It provides everything you need to create agents that can reason, take actions, and collaborate to solve complex problems.
| 2025/08/25 | Python | 🚀 ACP is now part of A2A under the Linux Foundation! 👉 Learn more |
https://github.com/orgs/i-am-bee/discussions/5
IBM Research launched the Agent Communication Protocol (ACP) in March 2025 to power its BeeAI Platform, an open-source platform exploring agent interpretability. Later that month, the BeeAI project—and with it, ACP—was donated to the Linux Foundation, solidifying our commitment to openly advancing agent interoperability as a community.
When the Agent2Agent Protocol (A2A) came on the scene a month later, we immediately saw alignment in how our teams approached the challenge of enabling agents to communicate and began exploring how to bring the efforts together.
Today, we’re excited to share that ACP is officially merging with the A2A under the Linux Foundation (@thelinuxfoundation) umbrella. This move is all about accelerating progress. “By bringing the assets and expertise behind ACP into A2A, we can build a single, more powerful standard for how AI agents communicate and collaborate,” says Kate Blair (@geneknit), Director of Incubation for IBM Research who has overseen ACP’s development.
As part of this transition, the ACP team will be winding down active development and will begin contributing its technology and expertise directly to A2A. Migration paths and documentation will be provided to help ACP users seamlessly move to A2A.
Blair will join the A2A Technical Steering Committee on behalf of IBM, alongside representatives from Google, Microsoft, AWS, Cisco, Salesforce, ServiceNow, and SAP.
For BeeAI users:
- Agents built with the BeeAI framework can be made A2A-compliant by using the A2AServer adapter.
- External A2A agents can be integrated into BeeAI applications using A2AAgent, enabling interaction with agents hosted elsewhere, regardless of how they were built.
- The BeeAI platform, previously powered by ACP, now uses A2A to support agents from any framework.
“We’re thrilled about what’s ahead and look forward to building the future of agent communication together,” says Todd Segal (@ToddSegal), A2A TSC member for Google and who co-led the initial development of the protocol.
The first issues aimed at bringing ACP features into ACP are already in the A2A github. Documentation updates are on the way for BeeAI, and users can get started with the new A2A-powered experience with the migration guide: BeeAI: ACP to A2A Migration Guide.
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