MCP vs. A2A: How Both Protocols Power Enterprise AI Agent Architecture

The Two Protocols Every AI Agent Team Needs to Understand

AI agents aren't research projects anymore. They're production systems making decisions, invoking tools, and collaborating with other agents across enterprise environments. And the teams building these systems keep running into the same architectural question: how should agents communicate?

Two open protocols have emerged to answer that question. The Model Context Protocol (MCP), originally created by Anthropic in November 2024, standardizes how agents connect to tools and external resources. The Agent-to-Agent Protocol (A2A), introduced by Google in April 2025, standardizes how agents discover and collaborate with each other.

Both are now governed by the Linux Foundation. Both are open standards with growing ecosystems. And both address real problems that engineering teams encounter the moment they move beyond a single-agent prototype.

The confusion in the market is understandable. The "MCP vs. A2A" framing suggests a competition, as if teams need to pick one. That framing is wrong. MCP and A2A solve different problems at different layers of the agentic stack. Understanding where each protocol operates, what it does well, and where it falls short is the first step toward building agent systems that actually scale in production.

This guide breaks down both protocols in detail, compares them directly, explains how they work together in production, and addresses the governance challenge that no one else is talking about: once you adopt both protocols, who controls the traffic?

What Is MCP (Model Context Protocol)?

The Problem MCP Solves: Tool Discovery and Invocation

Before MCP existed, connecting an AI agent to an external tool meant writing custom integration code for every API. Each tool required its own authentication flow, its own request format, its own error handling. If an API updated its versioning scheme or changed its endpoint structure, the integration broke. Multiply that across dozens or hundreds of tools, and the integration burden became a bottleneck that slowed every agentic deployment.

LLMs are designed to operate in natural language. They interpret a task, identify the right capability, and invoke it. But raw API handling by an LLM is unreliable and difficult to scale. APIs suffer from inconsistent documentation, versioning conflicts, and discoverability problems. An agent trying to use 50 different APIs needs to understand 50 different schemas, authentication methods, and response formats.

MCP was built to solve this. It provides a single, standardized protocol for how AI agents discover, connect to, and invoke external tools and resources. Instead of writing bespoke integrations for every tool, developers expose their capabilities through MCP servers, and any MCP-compatible client can connect to them using the same protocol.

How MCP Works: Architecture and Components

MCP follows a client-server architecture with three core participants:

• MCP Host: The AI application that coordinates everything. This could be Claude Desktop, VS Code with Copilot, or a custom agent built on any framework. The host manages one or more MCP clients.

• MCP Client: A component within the host that maintains a dedicated connection to a single MCP server. Each client handles the communication lifecycle with its corresponding server.

• MCP Server: A program that exposes tools, resources, and prompts to MCP clients. Servers can run locally (using stdio transport) or remotely (using Streamable HTTP transport).

The protocol itself has two layers:

Data Layer: Built on JSON-RPC 2.0, this layer defines the message structure and semantics. It handles lifecycle management (connection initialization, capability negotiation, termination), and exposes three core primitives:

• Tools: Executable functions that agents can invoke to perform actions, like querying a database, calling an API, or writing to a file system.

• Resources: Data sources that provide contextual information, like file contents, database records, or API responses.

• Prompts: Reusable templates that structure interactions with language models, such as system prompts or few-shot examples.

Transport Layer: Manages the actual communication channels. MCP supports two transport mechanisms:

• Stdio Transport: Standard input/output streams for local process communication. Zero network overhead, ideal for tools running on the same machine.

• Streamable HTTP Transport: HTTP POST for client-to-server messages with optional Server-Sent Events for streaming. This enables remote server communication and supports OAuth 2.1 for authentication.

The discovery flow is straightforward. A client connects to a server, negotiates capabilities during initialization, then calls tools/list to discover available tools. Each tool comes with a name, description, and JSON Schema defining its expected inputs. When the agent decides to use a tool, it sends a tools/call request with the appropriate arguments and receives a structured response.

MCP's Scalability Constraint

MCP works well for simple setups. One agent, a handful of MCP servers, each exposing a manageable number of tools. But there's a fundamental constraint that surfaces at scale.

Every tool connected to an MCP server sends its full description and schema to the LLM as part of the prompt context. Tool manifests, system instructions, conversation history, retrieved documents -- all of it competes for the same context window budget. As the number of tools grows, these descriptions can consume a significant portion of the available token space, even on models supporting 200K+ tokens.

The math gets unfavorable quickly. A single tool description with its JSON Schema might consume 200-500 tokens. Scale that to 100 tools and you're looking at 20,000 to 50,000 tokens consumed just by tool metadata, before any conversation, reasoning, or retrieved context enters the picture.

There have been improvements in reducing token consumption for MCP tool manifests. But even with optimization, the context window remains a hard ceiling. This isn't a bug in MCP. It's a consequence of how the protocol operates: at the tool level, with full schema visibility for the LLM. At a certain scale, that level of detail becomes the bottleneck.

This is precisely where A2A enters the picture.

What Is A2A (Agent-to-Agent Protocol)?

The Problem A2A Solves: Agent Discovery and Collaboration

MCP answers the question "how does an agent use a tool?" A2A answers a different question entirely: "how does an agent find and collaborate with another agent?"

As agentic systems grow beyond a single agent with a set of tools, teams inevitably need agents that can delegate work to other agents. A customer service agent needs to hand off billing questions to a billing specialist agent. A research agent needs to coordinate with a data retrieval agent and a summarization agent. A supervisory agent managing a complex workflow needs to discover which specialized agents are available and what each one can do.

Without a standard protocol for this, every agent-to-agent interaction becomes a custom integration. Teams end up building proprietary orchestration layers, tightly coupling agents to each other, and creating systems that are brittle and hard to extend. Cross-organization agent collaboration becomes nearly impossible.

A2A was designed to solve this. It provides an open, standardized protocol for agents to discover each other's capabilities, negotiate interactions, delegate tasks, and exchange results, regardless of what framework each agent was built with. An agent built on LangGraph can collaborate with an agent built on CrewAI or Semantic Kernel, as long as both speak A2A.

How A2A Works: Architecture and Components

A2A is built around several core concepts that work together to enable agent collaboration:

Agent Cards: The discovery mechanism in A2A. An Agent Card is a structured metadata document that describes what an agent can do at a high level, including its name, description, capabilities, supported interaction modes, and endpoint URL. Agent Cards are analogous to a business card for an agent. They tell other agents enough to decide whether to engage, without exposing internal implementation details.

Agent Cards are typically hosted at a well-known URL (e.g., /.well-known/agent.json) and can be discovered through registries, direct URL lookup, or other discovery mechanisms. The key design decision is that Agent Cards describe capabilities at a summary level. They do not list every tool or function the agent has access to. This keeps the discovery payload lightweight and avoids the context window scaling problem that MCP faces.

Task Lifecycle: Once an agent discovers another agent through its Agent Card, collaboration happens through tasks. A client agent sends a task to a remote agent, which processes it and returns results. Tasks support multiple states (submitted, working, input-required, completed, failed, canceled) and can involve multi-turn interactions where the remote agent asks for clarification or additional input before completing the work.

Message Exchange: Agents communicate through structured messages that can contain multiple content parts, including text, files, and structured data. This supports rich interactions that go beyond simple request-response patterns.

Streaming Support: For long-running tasks, A2A supports Server-Sent Events (SSE) so the client agent can receive incremental updates as the remote agent works through a task.

Push Notifications: For asynchronous workflows, A2A includes a push notification mechanism. A client agent can subscribe to updates on a task and receive notifications when the task status changes, without needing to poll.

A2A's Design Philosophy

Several design principles distinguish A2A from MCP and shape how it fits into the broader agentic architecture:

Agent opacity: Agents interact without sharing internal memory, tools, or proprietary logic. A billing agent doesn't need to know what tools a customer service agent uses, and vice versa. This preserves intellectual property and security boundaries between agents, which is especially important for cross-organization collaboration.

Framework independence: A2A doesn't prescribe how agents are built internally. Any agent that can expose an Agent Card and handle A2A's task protocol can participate, regardless of the underlying framework, language, or LLM powering it.

Agent-level abstraction: A2A operates exclusively between agents. It doesn't interact directly with tools or end systems. When an agent receives a task via A2A, it uses its own internal mechanisms (which may include MCP) to fulfill that task. This separation of concerns keeps A2A focused on collaboration while leaving tool execution to protocols designed for it.

Cross-domain collaboration: A2A is designed for scenarios where agents from different organizations, different cloud environments, or different security domains need to work together. The protocol includes support for authentication, agent identity verification, and trust negotiation to make cross-boundary collaboration possible.

MCP vs. A2A: A Direct Comparison

Scope: Tool Access vs. Agent Collaboration

The most fundamental difference between MCP and A2A is what they connect.

MCP connects agents to tools and resources. It operates at the primitive level, exposing individual functions, database queries, API calls, and file operations. When an agent needs to execute a specific action in the external world, MCP is the protocol that makes that happen.

A2A connects agents to other agents. It operates at a higher level of abstraction, facilitating collaboration between autonomous systems that each have their own reasoning, tools, and internal logic. When an agent needs to delegate a complex task to a specialist, A2A handles the discovery, negotiation, and communication.

Communication Pattern: Client-Server vs. Peer-to-Peer

MCP uses a strict client-server pattern. The MCP host (the AI application) creates clients that connect to servers. Communication flows in one direction: the client discovers and invokes tools exposed by the server. The server doesn't initiate requests to the client (though it can send notifications).

A2A supports more flexible communication patterns. While tasks flow from a client agent to a remote agent, the interaction can involve multi-turn exchanges where either party sends messages. The remote agent can request additional input, provide partial results, and negotiate the scope of the task. This is closer to a peer-to-peer conversation than a strict client-server invocation.

Discovery Mechanism: Tool Manifests vs. Agent Cards

MCP uses tool manifests for discovery. When a client connects to a server, it calls tools/list and receives the full schema for every available tool, including names, descriptions, and complete JSON Schema definitions for inputs and outputs. This gives the LLM detailed information about exactly what each tool can do and how to use it.

A2A uses Agent Cards for discovery. An Agent Card provides a high-level summary of an agent's capabilities without listing every internal tool or function. This is deliberately less detailed than an MCP tool manifest. The goal is to provide enough information for a supervisor agent to decide whether to route a task to a given agent, without consuming the context window budget that MCP's detailed schemas require.

Scalability Profile: Context Window vs. Network Complexity

MCP's scalability bottleneck is the LLM's context window. As tool count grows, tool schemas consume more tokens, leaving less room for conversation, reasoning, and retrieved context.

A2A's scalability challenges are network-level. As the number of collaborating agents grows, managing discovery, routing, authentication, and task coordination across many agents introduces complexity similar to managing a distributed system. But this complexity doesn't consume the LLM's context window, because Agent Cards are high-level summaries processed at the orchestration layer, not fed as detailed schemas into the model.

Security Model: OAuth 2.1 vs. Agent Identity Verification

MCP's security model centers on OAuth 2.1 with PKCE for authentication, dynamic client registration, and tool-level scoping. The specification recommends using bearer tokens and standard HTTP authentication methods for remote servers.

A2A's security model focuses on agent identity and trust verification. Agent Cards include authentication requirements, and the protocol supports verifying that a remote agent is who it claims to be before delegating sensitive tasks. For cross-organization scenarios, this includes mechanisms for establishing trust between agents that have never interacted before.

Ecosystem Maturity: Rapid Adoption vs. Enterprise-Led Growth

MCP has seen explosive adoption since its November 2024 launch. Within the first month, the GitHub repository attracted tens of thousands of stars. Today, the ecosystem includes thousands of MCP servers covering everything from database connectors to cloud platform integrations. The rapid growth is driven by MCP's clear, immediate value proposition: connect your agent to your tools using one standard protocol.

A2A's adoption has followed a slower, more enterprise-driven trajectory. This isn't because A2A is less important. It's because the problems A2A solves (multi-agent orchestration, cross-organization collaboration) only surface at a level of architectural maturity that most teams haven't yet reached. As single-agent systems evolve into multi-agent architectures, A2A adoption will accelerate.

MCP vs. A2A Comparison Table

Why "MCP vs. A2A" Is the Wrong Question

The Layer-2 / Layer-3 Mental Model

The most useful way to understand how MCP and A2A relate to each other comes from network engineering, an analogy explored by Cisco's Rob Barton.

In the early days of computer networking, networks were small and self-contained. A single Layer-2 domain (the data link layer) handled all communication. Devices could talk directly to each other within the local network. But as networks grew and needed to interconnect, Layer-2 alone could not scale. Routers and routing protocols (Layer-3, the network layer) were introduced to aggregate information at a higher level and route traffic between networks.

MCP is the Layer-2 of agentic AI. It provides detailed, direct access to tools within a defined scope. An agent connected via MCP can see every tool, understand every schema, and invoke any function. That level of detail is powerful within a bounded domain, but it doesn't scale indefinitely.

A2A is the Layer-3. It operates at a higher level of abstraction, using Agent Cards (analogous to routing tables) to describe capabilities in summarized terms. Instead of exposing every tool's schema, an Agent Card tells a supervisor agent: "I can handle billing inquiries" or "I specialize in RF analysis." The supervisor routes the task without needing to know the internal details.

Just as modern networks require both Layer-2 and Layer-3 to function, production-grade agentic systems will require both MCP and A2A. The question isn't which protocol to use. It's how to architect the full stack.

The Real Architecture: MCP + A2A Working Together

In practice, here is how MCP and A2A combine in a multi-agent system:

1. A supervisor agent receives a complex request, such as "analyze our Q1 customer churn and recommend retention strategies."

2. The supervisor uses A2A to discover specialized agents via their Agent Cards. It finds a data analysis agent, a CRM query agent, and a strategy recommendation agent.

3. The supervisor delegates sub-tasks to each specialist via A2A's task protocol.

4. Each specialist agent uses MCP internally to connect to its specific tools. The data analysis agent invokes database queries. The CRM agent calls Salesforce APIs. The strategy agent pulls from internal knowledge bases.

5. Results flow back through A2A as each specialist completes its task, and the supervisor synthesizes the final output.

In this architecture, A2A handles the orchestration layer (which agent should do what), while MCP handles the execution layer (how each agent actually does its work). Neither protocol replaces the other. They operate at different levels of the stack and solve different problems. And when the execution layer involves secure access to MCP servers, that's where governance becomes critical.

When to Use MCP Alone vs. MCP + A2A

Not every system needs both protocols. Here's a practical decision framework:

MCP alone is sufficient when:

• You have a single agent with a manageable set of tools (roughly under 30-50, depending on schema complexity)

• All tools are within the same trust domain

• There's no need for agent-to-agent delegation

• The context window budget can accommodate all tool schemas plus conversation

MCP + A2A becomes necessary when:

• You are building a multi-agent system where specialized agents handle different domains

• The total tool count exceeds what a single agent's context window can handle

• Agents need to be developed and deployed independently by different teams

• Cross-organization agent collaboration is required

• You need task delegation with multi-turn negotiation, not just tool invocation

The transition point usually arrives when a team realizes their single-agent system is either hitting context window limits or becoming too complex for one agent to manage effectively. That's the signal to introduce A2A for agent-level orchestration while keeping MCP for tool-level execution.

The Missing Layer: Security and Governance Across Both Protocols

Authentication and Authorization in MCP

MCP's security model is built around OAuth 2.1 with PKCE (Proof Key for Code Exchange). Remote MCP servers authenticate clients using bearer tokens, and the specification supports dynamic client registration so new clients can onboard without pre-configuration.

At the tool level, MCP supports scoping, meaning an authenticated client can be restricted to a subset of available tools based on their permissions. The protocol also includes consent mechanisms where the host application can prompt the user before executing sensitive tool calls.

However, MCP's authorization model is primarily connection-level. A client authenticates to a server, and that authentication governs what tools are accessible. There's no built-in mechanism for enforcing per-user, per-tenant, or per-agent policies that span multiple MCP servers. If an agent connects to five different MCP servers, each server handles its own authentication independently. This is one of the core MCP security challenges that enterprises face when scaling agent deployments.

Identity and Trust in A2A

A2A approaches security through agent identity verification. Agent Cards include authentication and security requirements, allowing a client agent to understand what credentials are needed before initiating a task.

The protocol supports delegation chains, where a user's identity and permissions can be propagated through a sequence of agents. For example, if User A asks Agent B to delegate a task to Agent C, the system can verify that User A's permissions are sufficient for the actions Agent C will take.

For cross-organization scenarios, A2A includes mechanisms for establishing trust between agents that belong to different security domains. This is essential for enterprise deployments where agents from different vendors or different business units need to collaborate without compromising security boundaries.

The Combined Security Challenge Nobody Is Solving

Here's the problem that every article about MCP vs. A2A skips over: what happens when you deploy both protocols together?

In a combined MCP + A2A architecture, the security surface includes:

• Agent-to-agent trust (A2A): Which agents are allowed to discover and communicate with which other agents?

• Tool-level access control (MCP): Which tools is a given agent allowed to invoke, and under what conditions?

• Full chain auditability: When a user requests something, and a supervisor agent delegates to a specialist agent, which then invokes a tool via MCP, can you trace the entire chain? User to agent to agent to tool?

• Policy consistency: Are the access control rules governing A2A routing consistent with the authorization rules governing MCP tool access? Or can an agent bypass tool-level restrictions by routing through a different agent?

These aren't theoretical concerns. In any enterprise deployment where multiple agents collaborate across multiple tool servers, the governance gap between protocols becomes a real attack surface. An ungoverned agent that can discover other agents via A2A and invoke any tool via MCP has effectively unlimited access to your systems.

The protocols themselves don't solve this. MCP specifies how to authenticate to a single server. A2A specifies how to verify agent identity. But neither protocol provides a unified governance layer that enforces policies across both protocol layers simultaneously.

Enterprise Architecture Patterns for MCP + A2A

Pattern 1: Single Agent with MCP Tools (Starter)

The simplest architecture. One supervisory agent connected directly to a set of MCP servers. The agent discovers tools, reasons about which ones to use, and invokes them directly.

Works well for: Prototypes, internal tools with limited scope, use cases with fewer than 30-50 tools.

Breaks down when: Tool count exceeds the context window budget, or the agent needs to coordinate with other specialized agents.

Governance: Straightforward. The agent authenticates to each MCP server, and tool-level permissions are managed per-server.

Pattern 2: Multi-Agent with A2A Discovery + MCP Execution (Scale)

A supervisor agent uses A2A to discover and delegate to specialized agents. Each specialist has its own set of MCP tool connections. The supervisor does not need to know what tools each specialist uses. It only sees Agent Cards describing high-level capabilities.

Works well for: Production systems with domain-specific agents (finance, HR, engineering), teams that need to develop and deploy agents independently.

Breaks down when: There is no centralized control over which agents can communicate with each other, which tools each agent can access, or what data flows between them.

Governance: Fragmented. Each specialist agent manages its own MCP connections. The supervisor trusts that each specialist will respect access boundaries, but there's no centralized enforcement.

Pattern 3: Governed Multi-Agent with Gateway Control (Enterprise)

The full enterprise architecture. A gateway layer sits between agents and tools, enforcing policies across both the A2A and MCP layers. The gateway controls:

• Which agents can discover and communicate with which other agents (A2A governance)

• Which tools each agent can invoke, and under what conditions (MCP governance)

• Identity propagation: ensuring the original user's permissions flow through the entire agent chain

• Audit logging: capturing the full trace from user request to agent delegation to tool invocation

• Data governance: controlling what data can flow between agents and what needs to be masked or redacted

Works well for: Enterprises deploying multi-agent systems at scale, regulated industries, any organization where ungoverned agent sprawl is a risk.

This is the only pattern that provides true visibility and control across both protocol layers. And it is the pattern that the current MCP vs. A2A conversation almost entirely ignores.

How Agen Governs the Full MCP + A2A Stack

This is where the architectural discussion becomes practical.

Agen (by Frontegg) is built to be the governance layer for agentic systems. It sits between humans, AI agents, and the applications they access, enforcing identity, permissions, and governance before any action occurs.

In the context of MCP + A2A architectures, Agen provides:

Unified identity for users and agents: One identity model that covers both human users and the agents acting on their behalf. When an agent invokes a tool via MCP or delegates to another agent via A2A, the system knows who initiated the action and whose permissions apply.

Tool-level access control (MCP governance): Agen's enterprise-grade MCP gateway provides fine-grained authorization over which tools each agent can access. This isn't just authentication to the MCP server. It's policy-based control over individual tool invocations, scoped by user, tenant, role, and context.

Agent-level routing governance (A2A governance): Agen controls which agents can discover and collaborate with which other agents. This prevents ungoverned agent sprawl where any agent can delegate to any other agent without oversight.

Full audit trail across both protocols: Every action, from the initial user request through agent-to-agent delegation to the final tool invocation, is logged and traceable. The chain is never broken. You can always answer the question: who did what, through which agent, using which tool, and on whose behalf.

Data governance and guardrails: Agen enforces data masking, redaction policies, and guardrails on agent actions. Data that flows between agents or gets exposed through tool responses is governed by policies defined once and applied everywhere.

Deployment flexibility: Agen deploys as a managed cloud service, within a customer's VPC, on-premises, or even on-device with a lightweight runtime. The governance layer lives where the data lives. For SaaS companies exposing their products to AI agents, Agen for SaaS provides the same governance for external agent traffic. For internal agent deployments, Agen for Work gives security teams visibility and control over employee-driven agent activity.

For teams building multi-agent systems that combine MCP and A2A, Agen provides the missing control plane. The protocols define how agents talk to tools and to each other. Agen governs who is allowed to talk to whom, what they're allowed to do, and what data they're allowed to see.

The Future of MCP and A2A

Protocol Convergence or Continued Specialization?

There's ongoing discussion about whether MCP and A2A will converge into a single protocol or continue as separate, specialized standards. The evidence so far points toward continued specialization.

Both protocols are governed by the Linux Foundation, which provides a neutral home for open standards. But their design philosophies address fundamentally different problems. MCP is optimized for tool-level precision. A2A is optimized for agent-level collaboration. Merging them would compromise the strengths of each.

What's more likely is tighter integration at the specification level, with clearer guidance on how MCP servers can be exposed as A2A-compatible resources and how A2A Agent Cards can reference MCP tool capabilities. The official A2A documentation already describes how A2A servers can expose skills as MCP-compatible resources for simpler, stateless interactions.

The Role of Gateways in Multi-Protocol Agent Systems

As both protocols mature, the gateway layer will become the critical architectural component. Just as API gateways became essential when organizations moved from monolithic APIs to microservice architectures, agent gateways will become essential when organizations move from single-agent prototypes to multi-agent production systems.

The gateway is where policy enforcement, identity propagation, rate limiting, audit logging, and data governance converge. It's the point of control in an otherwise decentralized system. Teams that deploy MCP + A2A without a governance gateway will face the same sprawl, visibility gaps, and security risks that teams faced when deploying microservices without API management.

From "Both Are Needed" to "Both Must Be Governed"

The current consensus in the market is that MCP and A2A are complementary. That consensus is correct but incomplete.

Saying both are needed is the starting point. The next step is recognizing that both must be governed. Complementary and ungoverned is a liability, not an architecture. The protocols define the communication standards. Governance defines who's allowed to communicate, what they're allowed to do, and who's accountable when something goes wrong.

The teams that recognize this early, that treat governance as an architectural layer rather than an afterthought, are the ones that will scale their agentic systems into durable, production-grade deployments.

Frequently Asked Questions

What is the difference between MCP and A2A?

MCP (Model Context Protocol) standardizes how AI agents connect to and invoke external tools, APIs, and data sources. A2A (Agent-to-Agent Protocol) standardizes how AI agents discover and collaborate with each other. MCP operates at the tool level (agent-to-tool). A2A operates at the agent level (agent-to-agent). They solve different problems at different layers of the agentic stack.

Is MCP better than A2A?

Neither protocol is universally better. They serve different purposes, and comparing them head-to-head misses the point. MCP is the right choice for connecting an agent to tools and resources. A2A is the right choice for enabling multi-agent orchestration and cross-domain collaboration. Most production systems will use both, with MCP handling tool execution and A2A handling agent coordination.

Can MCP and A2A be used together?

Yes, and this is the recommended approach for any system that involves multiple agents. A supervisor agent uses A2A to discover and delegate tasks to specialized agents. Each specialist agent uses MCP internally to invoke the specific tools it needs. A2A provides scalable agent-level routing. MCP provides precise tool-level execution.

Who created MCP and A2A?

MCP was created by Anthropic and released in November 2024. A2A was created by Google and released in April 2025. Both protocols are now governed by the Linux Foundation as open standards.

What is an Agent Card in A2A?

An Agent Card is a structured metadata document that describes an agent's capabilities at a high level. It includes the agent's name, description, skills, supported interaction modes, authentication requirements, and endpoint URL. Agent Cards allow other agents to discover what a given agent can do without needing to know its internal tools or implementation details. They function similarly to a well-known service description in microservice architectures.

Does MCP replace APIs?

MCP doesn't replace APIs. It provides a standard layer on top of APIs that makes them accessible to AI agents. MCP servers wrap existing APIs, databases, and services in a protocol that agents can discover, understand, and invoke using natural language. The underlying APIs still exist and function normally. MCP simply provides a consistent interface for AI agents to interact with them.

What are the security risks of using MCP and A2A together?

The primary risk is the governance gap between the two protocols. MCP handles authentication at the individual server level. A2A handles agent identity verification. But neither protocol provides a unified governance layer that enforces consistent policies across both. Without a centralized control plane, an agent could potentially bypass tool-level restrictions by routing through another agent, or access data that should be restricted by exploiting the gap between agent-level and tool-level permissions. A governance gateway that spans both protocols mitigates this risk.

How do you govern AI agents using both MCP and A2A?

Governance requires a gateway layer that sits between agents and the tools and agents they interact with. This layer enforces identity-aware access control across both protocols, maintains a full audit trail from user request through agent delegation to tool invocation, applies data governance policies (masking, redaction, guardrails) consistently, and provides observability into the entire agent chain. Agen is purpose-built for this, providing unified governance across both MCP tool access and A2A agent collaboration.