AI Agents Explained: MCP & A2A Protocols for Smarter Collaboration

With the rise of LLMs, the possibilities of artificial intelligence have expanded dramatically, from summarizing texts and answering complex questions to generating code, creating images, and supporting decision-making.

Behind many of these capabilities lies the concept of AI agents: systems that use LLMs as the “brain” to interact with the world, perform tasks, and autonomously achieve goals.

But this is only the beginning. By introducing protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol), the potential grows even further.

These protocols enable agents not only to connect with external tools and real-time data but also to collaborate, combining their different specializations to solve more complex problems.

Before diving into the protocols, it’s worth taking a moment to understand what AI agents are and how they work in practice.

Then, we’ll explore how to make them even more powerful through MCP and A2A, unlocking new possibilities for interaction and collaboration.

AI Agents

AI agents are autonomous software systems that perceive their environment, make decisions, and act to achieve human-defined goals.

Powered by LLMs, they can interpret inputs, generate plans, and decide on actions in a continuous loop rather than waiting for explicit step-by-step instructions. 

Typically, an agent combines three core elements: 

• A profile that defines its role and objectives, 
• Planning component (often LLM-based) that reasons over context and memory
• An action component that executes tasks through integrations with APIs, enterprise systems, or databases. 

Together, these elements turn agents from passive tools into proactive collaborators capable of handling complex workflows end to end.

A key characteristic of AI agents is their ability to decide and act autonomously. These actions can include querying databases, updating enterprise systems, running internal workflows, or calling external APIs.

In this setup, the LLM handles reasoning while integrations enable real-world execution. Without a standard, these integrations become complex to maintain, and that’s where the Model Context Protocol (MCP) helps, by providing a unified way for agents to connect with tools and perform tasks reliably

AI agents are most effective when working within a specific context, like scheduling, CRM, or finance.

This specialization makes them efficient, but it also creates a limitation: to solve broader problems, agents need a way to collaborate across different domains, and that’s where protocols like A2A come into play.

Also read: AI Agents vs AI Systems for Software Architecture

What is the MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic to make AI agents more powerful and interoperable. Its purpose is to provide a structured way for agents to connect with tools, services, and real-time data, enabling them to move beyond passive reasoning into actionable execution.

Instead of being tied to one-off integrations, MCP creates a common interface that allows any MCP-compliant agent to use external resources such as APIs, calendars, databases, or enterprise systems in a consistent way.

The problem MCP addresses is the integration bottleneck. Without it, every agent must be hard-coded to interact with each external service it needs, including custom authentication, API schemas, and error handling.

As the number of agents and tools grows, this quickly turns into an M×N problem: each agent must integrate with each service separately, making the system fragile and costly to maintain. MCP simplifies this by standardizing discovery and interaction, so that once a tool is described in MCP, any compliant agent can use it immediately.

How MCP works

Internally, MCP organizes the interaction between an AI agent and external tools into three main components:

• The Host is where the agent runs, for example, a chat interface or a copilot application.
• The Client acts as a bridge, translating the agent’s intentions into tool calls, handling communication, and enforcing authentication. 
• The Server is where the tools live, exposing capabilities like sending emails, querying a calendar, or accessing a database. This separation allows agents to remain model-agnostic while still gaining structured access to external services.

In most implementations, the Host (user-facing app) and Client (connector logic) reside together within the same application.

The Server, however, can be either an external API or implemented within the same application, depending on where the tools or services are hosted.

The communication between these components happens through well-defined payloads:

• Prompts include the system rules, chat history, and latest user request, essentially what the agent “sees” at each step. 
• Context provides the live data the agent needs to make better decisions, such as recent emails, calendar availability, or CRM records. 
• Tools are a catalog of callable actions, each described with a name, purpose, and JSON schema so the agent knows exactly how to invoke them. Together, these elements create a feedback loop where the agent can reason, plan, and act with both autonomy and structure.

MCP in Action: Use Case

Imagine a user asking their AI assistant: “Plan a business trip to New York next week.”

Without MCP, the agent would need custom-coded integrations for every external service involved, such as flights, hotels, calendar, email, and more. Each integration would be unique, fragile, and difficult to maintain.

With MCP, however, the process becomes seamless. The assistant can:

1. Use a flight booking tool (via an MCP server) to search and reserve tickets.
2. Access the user’s calendar through another MCP tool to check availability.
3. Call a hotel booking API to confirm lodging.
4. Send a confirmation email using an email tool.

The Host (chat interface) passes the request to the Client, which then orchestrates tool calls defined in MCP. Each tool is described with a clear schema, so the agent knows exactly what inputs are required and what outputs to expect.

In the end, the user receives a complete itinerary with flights booked, hotel reserved, and meetings scheduled, all within a single reasoning loop powered by MCP.

What is A2A (Agent to Agent Protocol)?

The Agent-to-Agent Protocol (A2A) is an open standard introduced by Google in 2025. It allows AI agents to discover, communicate, and collaborate with each other across different platforms.  

While MCP focuses on connecting agents to tools and data, A2A provides the rules for agents themselves to exchange tasks and coordinate their efforts.

The main problem A2A solves is the lack of interoperability between agents. Without a standard, each new agent must implement custom discovery, authentication, and message formats to interact with others.

This quickly becomes fragile and inefficient. A2A removes this barrier by defining a consistent protocol for discovery and communication, enabling specialized agents from different domains to collaborate seamlessly on complex tasks.

How A2A works

Internally, A2A organizes the interaction between AI agents into a few key components:

• Agent Card is a small JSON document that describes what an agent can do, where it is located, and how it can be reached. It acts like a digital business card that other agents can read to discover capabilities.
• Client Agent is the one that initiates a task, sending a request to another agent.
• Server Agent is the one that receives the request, processes the task, and returns the result. This clear separation makes it possible for specialized agents to collaborate without needing custom connections for each interaction. An agent can act as either a client or a server, depending on the situation, and these roles are not fixed.

The communication between agents happens through well-defined messages:

• Tasks describe the work to be done, move through a lifecycle (created, in progress, completed), and can produce results.
• Messages are the structured exchanges between agents, and they can contain multiple Parts.
• Parts can be plain text, JSON data, images, or even tables, allowing rich multimodal communication. Together, these elements ensure that agents not only exchange instructions but also context and artifacts, enabling true collaboration across domains.

The communication flow typically begins when a client agent reads an Agent Card to discover another agent’s capabilities.

It then creates a task request and sends it as a message. The server agent updates the task status as it progresses, for example, from “in progress” to “waiting for input” and finally “completed.”

Once finished, the server returns the result as an artifact, which may include text, structured data, or files. This lifecycle ensures transparency, coordination, and reliable collaboration between agents.

A2A in Action: Use Case

Imagine a company planning a product launch. A coordinator agent receives the request and needs help across multiple domains. Instead of calling every tool directly, it discovers specialized agents through their Agent Cards and delegates.

1. Discovery: The coordinator reads Agent Cards for a Scheduling Agent, a Budgeting Agent, and a Marketing Agent to confirm capabilities and endpoints.
2. Task delegation: It sends a task to the Scheduling Agent to book a venue and align stakeholder calendars. In parallel, it sends a task to the Budgeting Agent to validate costs and create a spending plan. It also tasks the Marketing Agent with creating a campaign brief and an announcement timeline.
3. Progress updates: Each agent updates task status as messages: in progress, waiting for input, completed. The Scheduling Agent might pause and request venue preferences. The Budgeting Agent can attach a cost table as a JSON part. The Marketing Agent can return a draft announcement as text and a media checklist as a file artifact.
4. Results aggregation: The coordinator receives artifacts from each agent and composes the final launch plan with venue booking, approved budget, and marketing assets. If any dependency changes, the coordinator resubmits or amends tasks and the agents continue from their current state.

A2A lets domain experts remain focused while collaborating through a consistent protocol for discovery, task exchange, status updates, and artifacts.

The result is a modular, cross-domain workflow without bespoke, brittle integrations between every pair of agents.

MCP and A2A Working Together

MCP and A2A work best when combined. MCP gives agents a standardized way to access tools and real-time data, while A2A provides the rules for agents to discover each other and collaborate across domains.

Together, they enable AI agents not only to act in the world but also to coordinate with one another, creating scalable multi-agent ecosystems.

Conclusion: The future of AI Agents with MCP and A2A

AI agents reach their full potential when they can both act in the world and collaborate across domains. MCP and A2A provide the standards that make this possible, paving the way for more scalable and interoperable agent ecosystems.

References

• What are AI agents? 
• Building AI Agents? A2A vs. MCP Explained Simply
• What Every AI Engineer Should Know About A2A, MCP & ACP
• Model Context Protocol – Official Documentation
• Agent2Agent Protocol – Official Documentation