Exec Summary

The Agent Communication Protocol (ACP) is an open standard that enables seamless communication and collaboration between AI agents, no matter what frameworks they’re built on. By providing a unified interface, ACP reduces integration complexity and promotes interoperability across multi-agent systems. This post shares key concepts and practical takeaways from the DeepLearning.AI course on ACP.

What is ACP?

ACP acts as a universal translator for AI agents, allowing them to communicate and collaborate without custom integrations—even if they’re built on different frameworks. This is similar to how HTTP standardised web communication, or how SQL (sort of) standardised database queries. ACP is about creating a common language for agents, enabling them to discover, message, and collaborate without needing to know the specifics of each other's implementations.

Why ACP?

Building multi-agent systems is hard. In practice, teams often end up with a patchwork of agents, each with its own quirks and integration headaches. Updating or swapping out an agent can mean rewriting large chunks of code. ACP tackles this by:

• Standardising agent communication: Agents can be swapped or upgraded without breaking the system.
• Promoting interoperability: Agents built with BeeAI, CrewAI, Smolagents, or other frameworks can work together out-of-the-box.
• Simplifying sharing and discovery: Agents can be registered and discovered across teams, making reuse much easier.

In short, ACP reduces friction and future-proofs your agentic systems.

How does ACP work?

At its core, ACP uses a client-server architecture:

• ACP Server: Wraps any agent (regardless of framework) and exposes it via a standardised interface.
• ACP Client: Connects to the server and sends requests, enabling integration and orchestration.

The DeepLearning.AI course on Agent Communication Protocol provides a hands-on introduction to building and using ACP agents. The course walks through practical steps:

• Wrap a RAG agent (using CrewAI) in an ACP server.
• Build an ACP client to interact with your server.
• Chain agents sequentially and hierarchically: For example, use a router agent to split a user query into tasks, delegating each to the right specialist agent.
• Mix and match protocols: Combine ACP with MCP (Model Context Protocol) for tool access, showing how these standards can work in tandem.
• Register agents on BeeAI: Make your agent discoverable and reusable by others.

Here's my course certificate!

Example Workflow

1. Build a RAG agent with CrewAI.
2. Wrap it in an ACP server.
3. Create a Smolagents agent, also wrapped in an ACP server.
4. Chain the two agents: the output of one feeds into the next.
5. Add a router agent to delegate tasks dynamically.
6. Register everything in the BeeAI platform for easy discovery.

Who is the course for?

If you’re working with AI agents and want to avoid integration nightmares, or just want to see what the future of agentic systems looks like, this course may be for you. Some Python experience is helpful, but the focus is on practical, hands-on learning.

Bottom line: ACP is a big step toward modular, maintainable, and scalable multi-agent systems. The course is short, practical, and packed with code examples—highly recommended if you’re building with agents in 2025.

Appendix - MCP vs ACP: How Are They Different?

The Model Context Protocol (MCP) is another important standard in the AI ecosystem, but it serves a different purpose than ACP. While both protocols aim to improve the functionality and interoperability of AI systems, they focus on different aspects of agentic AI. See also this eariler post: Meet MCP: The ‘USB-C’ for AI Integrations.

Here’s a quick comparison of the two protocols:

MCP is all about enriching the context that a single model or agent has access to; think of it as providing a language model with the right tools, data, and memory so it can reason more effectively. For example, MCP lets you plug in databases, APIs, or external tools so a model can answer questions or perform tasks with up-to-date information.

ACP, on the other hand, is designed for agent-to-agent communication. It lets multiple agents, potentially built with different frameworks, discover, message, and collaborate with each other to solve complex, multi-step problems. ACP is what enables a team of specialised agents to work together, passing tasks and information back and forth, rather than operating in isolation.

In short:

• MCP connects models to tools and data (vertical integration).
• ACP connects agents to each other (horizontal integration).

These protocols are complementary: for example, an agent might use MCP to access a database, then use ACP to delegate a sub-task to another agent. As agentic AI systems mature, using both together will be key for building robust, flexible, and scalable solutions.

A2A Protocol

As a side note, another protocol worth mentioning is Google’s A2A (Agent-to-Agent) protocol. Like ACP, A2A is designed to enable communication and collaboration between autonomous agents, focusing on interoperability and standardisation across agent frameworks. While ACP is an open standard with a growing ecosystem, A2A represents Google’s approach to agent interoperability, with some differences in implementation and adoption. Both protocols share the goal of making agent-based systems more modular and scalable, and their development signals a broader industry movement toward standardised agent communication.