The digital world is moving past simple web browsing to one where AI agents work together smoothly across different systems. This change needs a new way of setting things up, and a good answer is coming together with four important open-source parts.
- Agent2Agent (A2A) by Google: A plan to help agents find and talk to each other.
- Model Context Protocol (MCP) by Anthropic: A rule for how agents use tools and outside data.
- Apache Kafka: A strong system for sending messages, making sure things work well and are not too closely tied together.
- Apache Flink: A fast system for dealing with information as it comes in, important for making agent work better, watching it, and taking action.
This article looks at how these technologies work together, showing why we can’t just use plans alone and how this setup makes it possible to go from separate bots to active, smart agent groups.
We expect that companies will use many AI agents, each doing a specific job, instead of one agent that does everything. These agents will do things like write code, handle support tickets, look at customer data, help new employees get started, and watch over computer systems.
But, the tools we have now are not good enough for this future.
The problem is bigger than just agents not talking to each other. It includes a wider split in the whole system:
- No Agent Communication: Agents usually work alone. A customer agent doesn’t know what a data agent finds out. A support agent can’t fix problems that a monitoring agent sees.
- Tools Are Hard to Use: Without standard ways for agents to use tools or APIs, they have to use special connections that can’t be reused.
- Different Setups: Different agent systems use different models, seeing agents as chatbots, flowcharts, or planners. This means there’s no easy way to move agents or share information.
- Made for Notebooks: Many agents are made quickly as one-time projects. They do things in a straight line, at the same time, and only for a short time. But, real systems need to be able to retry things, handle failures, work together, keep records, and grow. This needs a system to support it.
- No Collaboration: There’s no way for agents to send messages to each other, share information, or keep track of what they’ve done. Information stays in direct calls or hidden in logs.
As the 12-Factor Agents project says, agents should follow cloud rules, being easy to watch, not closely tied to each other, easy to reproduce, and aware of the system they’re in. But, most are made as weak scripts, put together by hand and thought to work alone.
This causes problems, repeats work, and makes things fragile.
Agent2Agent helps with this by giving agents a standard way to find and talk to each other. But, to go from simple demos to systems that can handle the scale and reliability that real systems need, we need more than just plans. We need a full system.
The current agent world is like the early days of the web, with strong but separate and incompatible systems. Like the early problems of browsers talking to servers without a standard plan, AI agents today have trouble finding, talking to, and working with each other.
Google’s Agent2Agent (A2A): A Universal Protocol for Agent Communication
Google’s A2A plan is a big step in solving this problem. It’s not just another agent system, but a general plan made to connect any agent, no matter where it comes from or how it’s used.
Like how HTTP made website communication standard, A2A sets a common language for agents, letting them:
- Show What They Can Do: Using an
AgentCard, a description that says what an agent can do and how to talk to it. - Send and Receive Tasks: Through organized talks using JSON-RPC, where one agent asks for help and another gives back results or files.
- Send Updates with Server-Sent Events (SSEs): Giving real-time feedback during long or group tasks.
- Share Rich Content: Sharing files, organized data, and forms, not just simple text.
- Keep Security: Having built-in support for HTTPS, passwords, and permissions.
A2A is good because it doesn’t try to make new solutions. It uses well-known web standards, like HTTP and SMTP, making it easier to use and faster to connect.
But, A2A is only one part of the answer.
Anthropic’s Model Context Protocol (MCP): Standardizing Tool Usage and Context Access
Anthropic’s MCP looks at how agents use tools and get to data. MCP sets rules for how agents use APIs, call functions, and work with outside systems. It says how they work in their environment. While A2A controls how agents talk to each other, MCP focuses on how an agent works with the outside world.
Basically:
- MCP makes individual agents smart.
- A2A makes group intelligence possible.
Like how HTTP and SMTP needed a lot of use, system support, and developer tools to become popular, A2A and MCP will need a strong system to reach their full potential.
Even with standards like A2A and MCP, there’s a big question: How can agent talks grow across complex and changing company systems? Just using direct connections set by these plans causes problems with scale, strength, and being able to watch what’s happening. This shows we need a strong system to send messages.
Think of a company where employees can only talk through direct messages. To share an update, you’d have to message each person separately. To work on a project across teams, you’d have to manually send information between each group.
Growing such a system to hundreds of employees would be a mess.
This is like the problems in agent systems built on direct connections. Each agent has to know which agents to contact, how to reach them, and if they’re available. As the number of agents grows, the number of connections needed grows a lot, making a weak, hard-to-manage, and unscalable system.
A2A and MCP give agents the language and structure to talk and act. But, language alone isn’t enough. To coordinate many agents across a company, we need a system to manage messages and agent responses.
Kafka and Flink: The Backbone for Scalable Agent Collaboration
Apache Kafka and Apache Flink give this important system.
Kafka and Flink Explained
Apache Kafka, first made at LinkedIn and now an Apache Software Foundation project, is a system for sending event streams. It acts as a strong message bus, letting systems send and receive real-time event streams. Kafka is used in many applications, like financial systems, fraud detection, and telemetry pipelines, because it can separate producers from consumers and make sure data is durable, repeatable, and scalable.
Flink, another Apache project, is a system for processing streams in real-time. It’s made for handling events with state, high speed, and low delay. While Kafka manages data movement, Flink handles the changing, improving, watching, and organizing of data as it moves through a system.
Together, Kafka and Flink are a strong combination. Kafka is the bloodstream, while Flink is the reflex system.
Like A2A is the HTTP of the agent world, Kafka and Flink give an event-driven base for scalable agent communication and computation, solving problems that direct communication can’t:
- Decoupling: With Kafka, agents don’t need to know who is using their output. They send events (e.g., ‘TaskCompleted’, ‘InsightGenerated’) to a topic, letting any agent or system subscribe.
- Observability and Replayability: Kafka keeps a durable log of all events, making sure agent behavior is fully traceable, auditable, and repeatable.
- Real-time Decisioning: Flink lets agents react in real-time to event streams, filtering, improving, joining, or starting actions based on conditions.
- Resilience and Scaling: Flink jobs can grow independently, recover from failures, and keep state across long workflows, which is important for agents doing complex tasks.
- Stream-Native Coordination: Instead of waiting for responses, agents can work together through event streams, sending updates, subscribing to workflows, and moving state forward.
In short:
- A2A says how agents talk.
- MCP says how they use outside tools.
- Kafka says how their messages flow.
- Flink says how those flows are processed, changed, and used to make decisions.
Protocols like A2A and MCP are important for setting standards for agent behavior and communication. But, without an event-driven system like Kafka and a stream-native runtime like Flink, agents stay separate, unable to work together, grow efficiently, or reason over time.
The Four-Layer Architecture for Enterprise-Grade AI Agents
To fully make the vision of enterprise-grade AI agents real, we need a four-layer architecture:
- Protocols: A2A, MCP – saying what.
- Frameworks: LangGraph, CrewAI, ADK – saying how.
- Messaging Infrastructure: Apache Kafka – supporting the flow.
- Real-time Computation: Apache Flink – supporting the thinking.
Together, these layers make the new internet stack for AI agents, giving a base for building systems that are not only smart but also collaborative, observable, and ready for production.
We are at a crucial point in the evolution of software.
Just as the original internet stack – made of protocols like HTTP and SMTP and infrastructure like TCP/IP – started an era of global connection, a new stack is coming for AI agents. But, instead of humans browsing web pages or sending emails, this stack is made for autonomous systems working together to reason, decide, and act.
A2A and MCP give the protocols for agent communication and tool use, while Kafka and Flink give the infrastructure for real-time coordination, observability, and strength. Together, they let us move from disconnected agent demos to scalable, intelligent, production-grade ecosystems.
This change is not just about solving engineering problems. It’s about making a new kind of software where agents work together across boundaries, giving insights and driving actions in real time, letting intelligence become a distributed system.
But, this vision needs active development, focusing on openness, interoperability, and using the lessons learned from the previous internet revolution.
So, when making an agent, it’s important to think about how it fits within the bigger system. Can it talk well? Can it work with other agents? Can it change and adapt to new conditions?
The future is not just agent-powered; it’s agent-connected.