Imagine you’re managing a team where no one speaks the same language. One speaks technical English, another sends emojis, another only responds with GIFs… Total chaos, right? This is exactly the scenario the world of artificial intelligence found itself in until very recently. Each agent—these task-specific intelligences—spoke its own language—some built on LangChain, some using OpenAI, some in the cloud, some on-premises. They wanted to collaborate, but they couldn’t even order each other a coffee.
And then came three game-changing protocols:
- MCP (Model Context Protocol) – a type of interpreter that helps an agent access tools and understand what they need to do. For more details, read the article “MCP: the protocol that’s reshaping the way AI connects to the world”.
- ACP (Agent Communication Protocol) – the “WhatsApp for agents”: standardizes conversations between them.
- A2A (Agent-to-Agent Protocol) – the “corporate Slack for agents”: designed for scalability and security in the enterprise environment.
If agents were once islands, they are now forming archipelagos—connected, interoperable, and ready to work together. And this has everything to do with what we do in social listening: multiple intelligences need to interact to generate complex, fast, and contextual insights. Let’s understand how.
What is ACP? And why does it matter?
The Agent Communication Protocol (ACP) was created by IBM in partnership with BeeAI to solve a very practical problem: how to make AI agents from different backgrounds, languages, and frameworks communicate with each other?
Inspired by HTTP (which connects us to web pages), ACP connects agents. It defines a simple protocol, based on REST, that makes conversations between agents intelligent, standardized, and modular.
How does it work?
ACP follows a client-server architecture:
- ACP Server: hosts one or more agents, exposes their “talents” via REST API.
- ACP client: another agent or app that sends a request and receives the result.
Figure 1 shows the ACP client-server architecture.
Source: BeeAI and Linux Foundation
- 1. The ACP cliente discovers available servers and then initiates a request.
- 2. Is sends a REST request to the ACP server.
- 3. The server, which wraps an agent, manages the request and returna a response back to the cliente over REST.
This structure allows:
- A single agent is exposed.
- Multiple agents share the same server.
- Different servers communicate with each other over a network, forming an interactive ecosystem.
Oh, and by the way: ACP doesn’t care if the agent was built with LangChain, AutoGen, or CrewAI. Just package the agent with the default interface and voila—it’s in the game.
What about A2A? Where does Google fit into this conversation?
A2A (Agent-to-Agent Protocol) is Google’s response to the same challenge, with a more specific focus on complex enterprise environments. While ACP was born in the IBM lab with a community spirit, A2A comes with an enterprise pedigree, including security, robust authentication, OpenAPI compatibility, and long-running tasks.
It operates on a simple and powerful idea:
A customer agent makes a request → searches for another (remote) agent with the required skill → coordinates a “task” with a beginning, middle, and end.
And this may include:
- Exchanging messages with artifacts (texts, images, videos, forms);
- Synchronous or asynchronous flows;
- Agent discovery via “skill cards”.
Want your customer service agent to collaborate with your finance agent to generate a personalized proposal based on real data? A2A can solve the problem. And with enterprise security and OAuth authentication, everything is compliant.
Where does this all fit in?
The ACP protocol sits between the underlying models (such as LLMs), the cloud infrastructure, and the business applications. It is the brain that integrates intelligence (model) and action (application).
And remember MCP? It’s still useful. MCP is like an agent’s “internal Google”: it helps them find tools, databases, and APIs. ACP/A2A is what allows an agent to invite other agents to solve tasks together.Aplicações reais e inspiradoras
Want to see how all this works in practice?
- BeeAI (IBM): is an open-source agent orchestrator built with ACP. You can pair Aider (which writes code) with GPT-Researcher (which searches for information) and have them work together as a team. No drama, and best of all, no hacks..
- Google + Box: Box Inc. integrated A2A to allow its document analysis agents to communicate with other external agents (partners, cloud, etc.). The result? Intelligence where the data is, in real time..
- Smart supply chain: factory agents interact with carrier agents via ACP. The result: automated, scalable, and agile logistics planning. No need to develop 200 custom integrations.
- Automated recruitment: one HR agent initiates the process, another filters resumes, another schedules interviews, and another performs background checks. Everyone communicates via A2A, delivering a seamless experience for both the candidate and the company.
Eye-catching benefits (and ROI)
- Interoperability: no more workarounds to connect different APIs.
- Architectural scalability: each new agent can be plugged into the system like plugging in a USB stick.
- Security by design: with robust authentication, access control, and encryption.
- Modularity and agility: instead of one “do-it-all” agent, build a network of specialists.
- Open ecosystems: both ACP and A2A have open governance. Community, contribution, and evolution are guaranteed.
Conclusion: the sound of agents talking
We’re entering the era of AI talking to AI—and this isn’t science fiction. It’s the new normal.
For companies working with large volumes of data, such as social listening, this means having agents capable of monitoring trends, synthesizing insights, validating data with other expert agents, creating reports, and triggering alerts—all in a coordinated, secure, and efficient workflow.
ACP and A2A are the invisible threads that weave this new network of collaboration between intelligences. Whether your AI is shy or expansive, creative or analytical—with these protocols, it gains a voice, ears… and coworkers.
If you’re still thinking about how to connect your AIs, perhaps the right question is: how will you let them talk?
