The AI world is moving fast (very fast)— and it’s no longer just about bigger models. Models are the foundation and are more important than ever. However, the real frontier now is about how intelligent agents talk, cooperate, and interface. As autonomous systems multiply, they need shared languages to coordinate — the same way the Internet once needed TCP/IP and HTTP to make computers connect. Just like ~40 years back, when the foundation of internet was designed, this is the time when the foundation of Agentic AI is getting defined. how intelligent agents talk, cooperate, and interface Three emerging protocols are defining the agentic layers: MCP, A2A, and AGUI. Each solves a different part of the puzzle — how agents exchange context, collaborate with one another, and interact with humans. In this beginner friendly article, we’ll try to understand the basics and role of these protocols in under 3 minutes. MCP A2A AGUI The Agentic AI Stack If you zoom out, the modern agentic ecosystem looks less like a single framework and more like a stack of protocols. Each one defines how different parts of an intelligent system communicate — from raw tool execution to agent coordination to user interaction. stack of protocols At the foundation is MCP (Model Context Protocol), which standardizes how a system can expose its capabilities and how agents can invoke them. Above that sits A2A (Agent-to-Agent), the protocol that allows multiple agents to discover each other, delegate tasks, and share results. And at the top lies AGUI (Agent Graphical User Interface), the layer where humans meet agents through a consistent interface model. MCP (Model Context Protocol) how a system can expose its capabilities how agents can invoke them A2A (Agent-to-Agent) discover each other, delegate tasks, and share results AGUI (Agent Graphical User Interface) For further clarity, just as the Internet emerged from the layering of protocols — IP for routing, TCP for reliable transmission, HTTP for communication, and HTML for interaction — the agentic ecosystem is forming its own layered stack. At the foundation, the LLM/runtime layer executes tasks and functions, analogous to the TCP/IP layer of the Internet, providing reliable execution and delivery.
On top of that, MCP exposes agent capabilities and tools in a structured, discoverable format — much like APIs define services on the web.
Next comes A2A, the communication layer where agents exchange messages, delegate tasks, and coordinate workflows. This is the HTTP/WebSocket layer of agentic AI, enabling reliable message passing between autonomous systems.
Finally, at the top, AGUI standardizes human-facing interactions — the interface layer where users send commands and receive outputs. Think of it as the HTML layer of agentic AI, turning agent responses into readable, actionable experiences. At the foundation, the LLM/runtime layer executes tasks and functions, analogous to the TCP/IP layer of the Internet, providing reliable execution and delivery. foundation LLM/runtime layer TCP/IP layer of the Internet On top of that, MCP exposes agent capabilities and tools in a structured, discoverable format — much like APIs define services on the web. MCP Next comes A2A, the communication layer where agents exchange messages, delegate tasks, and coordinate workflows. This is the HTTP/WebSocket layer of agentic AI, enabling reliable message passing between autonomous systems. A2A HTTP/WebSocket layer of agentic AI Finally, at the top, AGUI standardizes human-facing interactions — the interface layer where users send commands and receive outputs. Think of it as the HTML layer of agentic AI, turning agent responses into readable, actionable experiences. AGUI HTML layer of agentic AI There is another view to look at the positioning of these protocols: Together, these form the “TCP/IP stack” of Agentic AI — the scaffolding that will eventually let agents, models, and humans coexist across systems, vendors, and interfaces. “TCP/IP stack” of Agentic AI The above analogy helps us in correlating modern agentic AI protocols with protocols that have been there for the last 30-40 years. Going down from the bird's-eye view, let’s get into each protocol now. MCP — The Capability Layer MCP (Model Context Protocol) defines a standardized way for agents to expose their capabilities and tools. It’s no longer just about “model context” — it’s about creating a discoverable, structured interface that any agent or client can call. The name is misleading. standardized way for agents to expose their capabilities and tools discoverable, structured interface Why it matters: Why it matters: Services /Agents can register what they can do, including tools, actions, and schemas.
Clients or other agents can query capabilities and invoke them without hard-coded integrations.
MCP is transport-agnostic, usually using JSON over WebSockets, HTTP streams, or other channels. Services /Agents can register what they can do, including tools, actions, and schemas. Clients or other agents can query capabilities and invoke them without hard-coded integrations. MCP is transport-agnostic, usually using JSON over WebSockets, HTTP streams, or other channels. transport-agnostic A good analogy will help in understanding it better: Think of MCP as the API schema layer of agentic AI — like OpenAPI or gRPC in the web world. It defines what services exist and how to call them, making interoperability possible across heterogeneous agent ecosystems. API schema layer of agentic AI what services exist how to call them A2A — The Communication Layer A2A (Agent-to-Agent) is the messaging and coordination protocol that standardizes how agents can talk to each other. It handles task delegation, structured messaging, and response collection. messaging and coordination protocol Why it matters: Why it matters: Supports multi-agent collaboration and orchestration.
Agents can send requests, delegate sub-tasks, and await structured responses.
Works over standard transports like WebSocket or gRPC, decoupling communication from execution. Supports multi-agent collaboration and orchestration. Agents can send requests, delegate sub-tasks, and await structured responses. Works over standard transports like WebSocket or gRPC, decoupling communication from execution. A good analogy will help in understanding it better:A2A is theHTTP/WebSocket layer of agentic AI — the “network” for agent conversations. Just as HTTP delivers requests and responses between servers, A2A delivers messages and tasks reliably between agents. HTTP/WebSocket layer of agentic AI AGUI — The Interface Layer AGUI (Agent Graphical User Interface) standardizes human-facing interaction with agents. It handles UI state, input capture (text, voice, visuals), and output rendering. human-facing interaction Why it matters: Why it matters: Ensures consistent and portable interfaces across applications.
Translates agent responses into human-readable or actionable forms.
Supports multimodal interaction and dynamic UI updates. Ensures consistent and portable interfaces across applications. Translates agent responses into human-readable or actionable forms. Supports multimodal interaction and dynamic UI updates. Like earlier, a good analogy helps here too:AGUI is theHTML/DOM layer of agentic AI — it defines how humans see and interact with agents, just like HTML structures the web page for users. HTML/DOM layer of agentic AI how humans see and interact Putting It Together: Real-World Agentic AI in Action Finally, let’s apply this knowledge to a real-world agentic AI system. Imagine a supply chain optimization agent tasked with managing inventory, coordinating shipments, and updating stakeholders in real-time. Here's how the Agentic AI stack facilitates this process: supply chain optimization agent AGUI (Agent Graphical User Interface): The process begins when a warehouse manager interacts with a dashboard displaying real-time inventory levels and shipment statuses. The AGUI layer translates the manager's inputs — such as initiating a restock order or adjusting delivery schedules — into structured data that the system can process.
A2A (Agent-to-Agent Protocol): Upon receiving the manager's request, the system's central agent communicates with specialized agents responsible for inventory management, logistics, and supplier coordination. Using the A2A protocol, these agents exchange messages to delegate tasks, share data, and ensure alignment across the workflow.
MCP (Model Context Protocol): As tasks are delegated, agents utilize the MCP to access and invoke external tools and APIs. For instance, an agent might use MCP to retrieve real-time shipping data from a third-party logistics provider, ensuring that the latest information informs decision-making.
LLM/Runtime Layer: The runtime environment executes the necessary computations, such as calculating optimal restock quantities or determining the best shipping routes. It processes the data, applies relevant models, and generates actionable insights or decisions.
AGUI (Feedback Loop): Once the computations are complete, the results are sent back through the A2A protocol to the AGUI layer, where the warehouse manager receives updated information, such as new inventory levels or revised delivery schedules, enabling informed decision-making. AGUI (Agent Graphical User Interface): The process begins when a warehouse manager interacts with a dashboard displaying real-time inventory levels and shipment statuses. The AGUI layer translates the manager's inputs — such as initiating a restock order or adjusting delivery schedules — into structured data that the system can process. AGUI (Agent Graphical User Interface): A2A (Agent-to-Agent Protocol): Upon receiving the manager's request, the system's central agent communicates with specialized agents responsible for inventory management, logistics, and supplier coordination. Using the A2A protocol, these agents exchange messages to delegate tasks, share data, and ensure alignment across the workflow. A2A (Agent-to-Agent Protocol): MCP (Model Context Protocol): As tasks are delegated, agents utilize the MCP to access and invoke external tools and APIs. For instance, an agent might use MCP to retrieve real-time shipping data from a third-party logistics provider, ensuring that the latest information informs decision-making. MCP (Model Context Protocol): LLM/Runtime Layer: The runtime environment executes the necessary computations, such as calculating optimal restock quantities or determining the best shipping routes. It processes the data, applies relevant models, and generates actionable insights or decisions. LLM/Runtime Layer: AGUI (Feedback Loop): Once the computations are complete, the results are sent back through the A2A protocol to the AGUI layer, where the warehouse manager receives updated information, such as new inventory levels or revised delivery schedules, enabling informed decision-making. AGUI (Feedback Loop): Conclusion The rise of agentic AI is not just about smarter models — it’s about creating a structured ecosystem of protocols that lets agents, tools, and humans collaborate seamlessly. The Agentic AI stack — AGUI, A2A, MCP, and the LLM/runtime layer — provides a layered framework that mirrors the Internet stack: AGUI like HTML for human interaction, A2A like HTTP for messaging, MCP like APIs for capability exposure, and the runtime like TCP/IP for execution. structured ecosystem of protocols Agentic AI stack

Top Agentic AI Protocols in 3 Minutes: MCP, A2A, AGUI

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Formal Analysis of Agentic AI Protocols: A2A, ACP, and AGUI

The AI Co-Pilot for a Star: Google DeepMind and the Quest for Fusion Energy

The New Open AI Agent Builder is Not an Agent Builder At All

How I Built a Production-Grade AI Prompt That Writes Executive Summaries in 30 Minutes

Daniel Saks Predicts Agentic AI Will Empower Individuals and Boost Productivity

A Formal Analysis of Agentic AI Protocols: A2A, ACP, and AGUI

The AI Co-Pilot for a Star: Google DeepMind and the Quest for Fusion Energy

The New Open AI Agent Builder is Not an Agent Builder At All

How I Built a Production-Grade AI Prompt That Writes Executive Summaries in 30 Minutes

Daniel Saks Predicts Agentic AI Will Empower Individuals and Boost Productivity

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps