Hiring at scale is hard. Take Amazon, for example — in 2021, they received over 30 million job applications in a single year. Now imagine trying to screen, shortlist, and schedule interviews for just a fraction of those without burning out your team. That’s where AI agents come in — not just smart assistants, but ones that can work with each other , across tools and systems.
But here’s the catch: building those agents isn’t just about picking a model. You’ll need a way to manage what agents know (that’s where MCP steps in), and another to make sure they can work together (enter A2A).
So, if you’re a business leader, product owner, or developer thinking about rolling out AI agents — understanding MCP vs A2A could save you time, avoid confusion, and help your agents actually get things done. Let’s break it down.
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The Role of MCP — Context, Tools, and Human-in-Loop Logic What is MCP? MCP, or Model Context Protocol, is like the planning phase before an AI agent takes action. It helps the agent understand what the user wants, chooses the right tools for the job, and sets the stage with the right context. Think of it as the agent’s way of asking, “What’s the task, and what do I need to know or use to handle it properly?” It brings structure and clarity before anything is actually done.
Key Functions of MCP 1. Query Routing This is where the user’s request is sent to the most suitable language model . It ensures the query is understood by the right system, which can then decide what to do next. It’s like choosing the right expert to answer a question.
Once the query is understood, the language model picks the right tool or backend service to handle it. This could be a calculator, a database, or another app. The agent doesn’t guess — it selects based on context.
3. Server Handoff After picking the right tool, the agent passes the task to that tool or server to carry out the actual work. This handoff makes sure the request is handled where the real processing happens — like sending a work order to the right team. It manages authentication, data formatting, and communication protocols , allowing the agent to leverage powerful external resources while maintaining a seamless user experience.
4. Human Checkpoints For sensitive or critical tasks, MCP can ask for human approval before continuing. It adds a safety layer, allowing people to review or confirm certain steps before the agent goes ahead. This is especially useful in areas like finance, hiring, or legal work.
Why MCP Matters? 1. Prevents Errors Before They Happen By setting up the right tools and context before any action is taken, MCP reduces the chance of misunderstandings or wrong outputs. It acts like a checklist before starting a task — making sure the agent knows exactly what to do and how to do it, rather than just guessing and acting too soon.
2. Adds Accountability and Context Depth MCP keeps track of who did what, using which tools, and why. It doesn’t just handle tasks — it explains the logic behind each step. This kind of traceability helps teams understand how a decision was made, making it easier to audit actions or improve agent behavior over time.
3. Great for Controlled Environments Like Finance or Healthcare In industries where mistakes are costly — like handling patient data or financial records — MCP adds structure and safeguards. It ensures agents don’t go off-track, and sensitive steps can include human checks. That control is key when accuracy and compliance aren’t optional.
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The Role of A2A — Collaboration Between Agents What is A2A? A2A, or Agent-to-Agent protocol, enables different AI agents to communicate and collaborate across various platforms and systems. It’s designed to make sure agents, regardless of the underlying technology or vendor, can seamlessly interact with each other, share tasks, and manage workloads. Think of it as creating a network of specialized agents that can work together, even if they’re using different tools or platforms.
This protocol empowers businesses by offering a standardized way to manage multiple agents, ensuring smooth collaboration in tasks that involve cross-functional or cross-platform systems. A2A allows for better coordination, flexibility, and efficiency across enterprise workflows.
Key Features of A2A 1. Agent Card An Agent Card is a public document that details an agent’s capabilities, skills, and authentication needs. It acts as a profile, showing other agents what this particular agent can do, what tools it can use, and how to connect securely . It helps agents discover one another and ensures they select the right one for a task.
2. Task System The Task System is the core of A2A, where each task goes through a defined lifecycle. Starting with task creation, it progresses through different stages (e.g., in progress, completed) until it reaches the final state. This system ensures that tasks are organized, tracked, and completed systematically.
3. Secure Messaging A2A includes built-in support for secure and authenticated messaging between agents. This feature ensures that communication stays safe, respecting enterprise-level security standards. It helps protect sensitive data, ensures privacy , and guarantees that only authorized agents can access or handle specific tasks.
4. Long-running Support A2A supports tasks that take hours, days, or longer to complete. Whether it’s gathering data or conducting in-depth research, agents can stay connected throughout the process. They send real-time updates , keeping all involved parties informed of progress, so tasks can evolve smoothly over time without losing track or requiring constant attention.
5. Modality Flexibility A2A is not limited to just text-based communication. It supports a wide range of modalities like audio, video, and web forms, enabling agents to handle more diverse tasks. This flexibility allows for a richer, more interactive experience, where agents can work together through different formats, depending on the needs of the task at hand.
Where A2A Fits Best 1. Multi-agent Ecosystems A2A is ideal for systems where multiple agents need to interact and collaborate across different platforms or tools. In a multi-agent ecosystem, each agent specializes in a specific task or function. A2A allows these agents to communicate seamlessly, share information, and coordinate actions, creating a unified, efficient system without being limited by different vendor technologies or frameworks.
In businesses that rely on various third-party tools across different departments, A2A allows agents to integrate these systems smoothly. Whether it’s HR, sales, or finance, A2A lets agents in each department collaborate, ensuring they can share tasks, pass information, and maintain consistent workflows without needing to replace existing tools or platforms.
3. Tasks That Need Handoffs Between AI Workers For complex tasks that require different AI agents to step in at various stages, A2A is key. It supports seamless handoffs between agents, allowing each one to take over when necessary. This ensures continuity and avoids bottlenecks, especially in multi-step tasks like data processing , candidate sourcing, or customer service, where multiple agents collaborate for a successful outcome.
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MCP vs A2A: How Are They Different? 1. Purpose MCP : Designed for managing context effectively, MCP ensures that the right tools and background information are in place before any task is carried out. It helps agents make informed decisions based on a clear understanding of the situation.
Organizes and sets up the environment for accurate results. Focused on preparation and planning for AI agents. A2A : Focuses on enabling collaboration between different agents, allowing them to work together across diverse systems, share tasks, and achieve collective goals.
Facilitates task delegation between agents. Ensures smooth communication and coordination between various agents. 2. Key Actors Involved MCP : The main actors here are the MCP Client (typically the user interface or initiating system) and the Large Language Model (LLM), which processes and decides what needs to be done.
The MCP Client is responsible for sending queries. A2A: The key players are the Client Agent and the Remote Agent. The Client Agent initiates tasks and communicates needs, while the Remote Agent executes those tasks.
The Client Agent formulates the task. The Remote Agent is responsible for carrying out the task on behalf of the Client Agent. MCP : Primarily uses internal tools and LLMs for managing context and decision-making. These tools work behind the scenes to ensure that the right information is available to the AI agent.
Relies on internal resources within the system. The LLM helps select the appropriate tool based on the query and context. A2A : Uses any agent that has an Agent Card — a public profile that defines its capabilities, tools, and how to connect. This flexibility enables agents from different systems and vendors to collaborate.
Agent Cards serve as the bridge for agent discovery. Supports a broad range of tools across platforms.
4. Task Type MCP : Works with pre-structured, goal-driven tasks. The tasks are well-defined, and the protocol focuses on solving specific problems with a clear outcome.
Tasks are usually designed and pre-defined by the client. Perfect for tasks with a defined beginning, middle, and end. A2A : Supports flexible, distributed tasks that may evolve over time. These tasks can change as agents interact and collaborate.
Allows for ongoing collaboration and adjustments. Perfect for dynamic environments where the exact steps aren’t always known upfront. 5. Protocols Used MCP : Uses custom protocols and workflows, often including approval mechanisms where human input is required, particularly in sensitive areas like finance or healthcare .
Works on internal protocols for context management. Approval workflows add a layer of oversight for accuracy. A2A : Built on common, open protocols such as HTTP, JSON-RPC, and SSE (Server-Sent Events). These are widely recognized and easily integrated into existing systems.
Supports standardized communication for cross-agent collaboration. Protocols enable real-time updates and seamless interactions. 6. Memory Sharing MCP : Supports memory sharing, meaning agents can retain context from previous interactions, enabling continuity in long-term tasks or conversations.
Contextual memory helps agents recall past interactions and adapt accordingly. Ensures more accurate and relevant responses over time. A2A : Does not directly share memory but can send context messages to provide necessary background for tasks. This approach maintains a level of flexibility while limiting direct memory-sharing.
Memory is task-specific and may not persist across interactions. Allows for context sharing, but not long-term retention. 7. Real-Time Updates MCP : Provides optional real-time updates depending on the task and setup. It’s more about setting up the right context and ensuring everything is in place.
Updates are task-specific and may not always be required. Suitable for environments where accuracy before execution matters more than immediate feedback. A2A : Designed with real-time updates at its core, A2A ensures that agents can constantly communicate and provide progress on tasks.
Instant updates keep all parties in the loop. Especially important for tasks requiring dynamic changes and feedback. 8. Modality Support MCP : Primarily supports text-based communication, which is ideal for managing queries, responses, and context in a clear and structured format.
Focuses on textual input and output. Well-suited for tasks that don’t need multimedia. A2A : Offers a wider range of modalities, including text, audio, video, and structured UI elements. This enables agents to collaborate across diverse communication formats and handle more complex interactions.
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Feature MCP A2A Purpose Context management Agent collaboration Main Actor MCP Client + LLM Client Agent + Remote Agent Tools Used Internal tools, LLMs Any agent with an Agent Card Task Type Pre-structured, goal-driven Flexible, distributed Protocols Used Custom with approval workflows HTTP, JSON-RPC, SSE Memory Sharing Yes No (but can send context messages) Real-Time Updates Optional Real-time supported Modality Support Mostly text-based Text, video, audio, structured UI
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MCP vs A2A: Example Scenarios to Understand the Use Cases Better MCP Use Case: Processing Expense Reports in Finance In the finance department, a team uses MCP to set up an AI agent designed to process expense reports. When an employee submits an expense report, the AI agent first analyzes the data by retrieving the necessary context—like company policies, allowable limits, and historical spending. This is where MCP steps in. It ensures that the agent understands the context, checks for compliance, and applies relevant rules before proceeding with any action.
If the report is flagged for review, the agent asks for human input (through a human-in-loop mechanism), ensuring that any complex or non-standard expense gets properly reviewed. The process is structured, and the agent can pull in the necessary tools to verify receipts, calculate totals, or check for discrepancies. This structured, rule-based approach makes sure the task is completed with accuracy, ensuring that every step adheres to compliance requirements.
Why MCP is Perfect for This Case It offers clear context management by ensuring the agent understands the business rules. The human-in-loop feature ensures that any discrepancies or non-standard items are flagged for review. The task is pre-structured and goal-driven, with clear steps that are easily manageable by the AI agent. A2A Use Case: Managing the Hiring Process In a hiring process, a hiring manager sets up an AI agent to help streamline the recruitment of new candidates. The manager inputs specific job requirements, like skills, location, and experience, into the system. The Client Agent, using A2A, starts by sending a task to a Remote Agent specialized in sourcing candidates. The Remote Agent accesses job boards, scans resumes, and identifies potential candidates, then sends the list back to the Client Agent.
After narrowing down the list, the AI agent communicates with other specialized agents. One might be in charge of scheduling interviews, while another could handle background checks. The agents coordinate with each other, each performing a specific task within the process, seamlessly passing information and statuses as they go. The collaboration between agents happens in real-time, allowing the hiring manager to see instant updates and adjustments based on new data or evolving needs.
Why A2A Works Well Here The task system enables clear delegation of steps like resume screening, interview scheduling, and background checks. Real-time updates keep the hiring manager in the loop about the progress of each task. Modality flexibility allows agents to interact across various formats, whether it’s handling text-based resumes, scheduling via email, or conducting video interviews.
MCP vs A2A: When to Use What Use MCP If Your Workflow 1. Requires Tight Control and Audit Trails MCP is ideal for environments where control, tracking, and traceability are crucial. For example, in financial services, compliance with regulations and ensuring every action can be audited is a must.
With MCP, you can ensure that every step an AI agent takes is logged, making it easy to track and review decisions. This makes it highly suitable for industries like healthcare or finance , where accountability is non-negotiable.
MCP excels when workflows need to dynamically choose the right tool or ensure compliance with specific rules. Imagine a legal tech platform where an agent must choose the right legal document template based on the situation.
With MCP, the agent can select the best tool (e.g., a contract generator or compliance checker) based on the context of the query, ensuring compliance with local regulations or company policies.
3. Needs Single-Agent Decision-Making with Strong Memory If your tasks require a single agent to manage ongoing processes, memory sharing is key. For example, a customer support agent might need to manage a multi-step service case where it remembers customer preferences, past issues, and resolutions.
MCP enables this kind of context management, ensuring that the agent maintains continuity and doesn’t lose sight of important details over time.
Use A2A If Your Workflow In scenarios where multiple specialized agents from various vendors need to interact, A2A shines. For instance, in enterprise IT management, different AI agents could be managing different tools — one agent manages the helpdesk, another handles incident tracking, and another monitors network security.
A2A enables seamless communication between agents from different platforms, allowing them to share data and complete tasks that require cross-system collaboration.
2. Requires Coordination Between Specialized Agents A2A is perfect when you need specialized agents to work together, each focusing on a unique part of a task. For example, in supply chain management , one agent might track inventory levels, another might monitor shipping logistics, and another handles demand forecasting.
These agents must collaborate to ensure a smooth operation, and A2A provides the framework for them to communicate and work together efficiently.
3. Involves Long or Multi-Step Tasks For workflows that span a long duration or involve many stages, A2A supports long-running tasks where agents need to take over specific parts of the process. Consider a product development pipeline where one agent handles market research, another performs prototyping, and a third manages testing and deployment.
These tasks can take hours or even days, and A2A enables continuous communication, real-time updates, and coordination throughout the entire lifecycle of the task.
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MCP and A2A: Why Do You Need Both? A2A and MCP complement each other, offering distinct advantages for different aspects of AI workflows. MCP provides the structure and context needed for a single agent to make informed decisions, ensuring control, compliance, and memory in complex tasks. On the other hand, A2A enables seamless collaboration between multiple agents across systems, supporting long-running tasks and specialized coordination.
By combining both, businesses can leverage the strengths of structured decision-making with MCP and dynamic, flexible agent collaboration with A2A. Together, they create a powerful, integrated AI system capable of handling a wide range of enterprise tasks efficiently.
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Frequently Asked Questions What is the A2A protocol? A2A (Agent-to-Agent) is a protocol that enables seamless communication and collaboration between AI agents across different platforms. It allows agents to share tasks, interact, and coordinate actions, regardless of the underlying technology, improving cross-functional workflows and multi-agent ecosystems within enterprise
What is the MCP protocol? MCP (Model Context Protocol) is designed to manage the context and tools AI agents need before performing tasks. It ensures agents have the right information, environment, and tool selection to make informed decisions, particularly for single-agent workflows in industries that require high control, compliance, and memory.
What is MCP and what is it used for? MCP helps AI agents understand the context of tasks by providing structured information and selecting the right tools. It’s widely used in scenarios that need strict compliance, dynamic tool selection , and context-based decision-making, such as finance, healthcare, or legal applications, ensuring that tasks are performed accurately and securely.
Is OpenAI adopting MCP? There’s no direct confirmation that OpenAI is adopting MCP specifically, but OpenAI’s model architecture supports principles similar to MCP, such as ensuring agents understand context and select the right tools. MCP’s concepts align with the overall goals of OpenAI’s initiatives to make AI more context-aware and versatile in complex environments.
How does A2A work? A2A facilitates task-sharing and communication between AI agents. A client agent sends a task request to a remote agent, which performs the task. Throughout the process, agents collaborate, share updates, and coordinate actions in real-time, ensuring that complex, multi-step tasks are completed efficiently across various platforms and systems.
How do MCP and A2A complement each other? MCP focuses on providing context and structuring individual agent workflows , while A2A enables communication between multiple agents across systems. Together, they create a comprehensive AI ecosystem where agents are both well-informed and capable of collaborating to handle complex tasks.
When should businesses choose MCP over A2A? MCP is best suited for tasks that require tight control, memory sharing, and compliance checks, such as in regulated industries. A2A is preferred when tasks involve multiple agents, require collaboration across systems, or need real-time updates and coordination.
Can MCP and A2A be integrated into existing workflows?
