A2A vs MCP vs ACP — Complete Protocol Comparison (2026)
Three protocols have emerged to define how AI agents talk to tools, to services, and to each other. They're not competing — but most teams confuse them. This is the complete breakdown: what each one does, how it works technically, and how to choose.
Contents
Quick Answer #
TL;DR — Three protocols, three layers
The key insight: MCP and A2A solve different problems at different layers of the stack and are designed to be used together. ACP is a conceptually similar alternative to A2A, most relevant within the IBM BeeAI ecosystem.
The Problem They're Solving #
As AI systems grow from single models into networks of specialized agents, a critical infrastructure question emerges: how do these agents communicate?
A single agent needs to connect to external tools — file systems, APIs, databases, search engines. That's one problem. A fleet of specialized agents needs to discover each other, delegate tasks, share context, and return results — reliably, across organizations and vendor boundaries. That's a different problem entirely. These two problems require different protocol designs.
Three protocols have stepped up to address this. They were created by different organizations, for different layers of the stack, and they complement each other far more than they compete.
Deep Dive: MCP (Model Context Protocol) #
Model Context Protocol
MCP connects an LLM to external tools, data sources, and resources. Think of it as the bridge between a model and the world it can act on — file systems, APIs, databases, and beyond.
- Client-server architecture (host app → MCP client → MCP server → tool)
- Three primitives: Tools, Resources, and Prompts
- Transport: stdio (local) or HTTP + SSE (remote)
- Direction: vertical — model gains capabilities from the world
- Widely adopted: Claude, VS Code, Cursor, Zed, Sourcegraph, JetBrains
How MCP works
The host application (e.g., Claude Desktop or Cursor) embeds an MCP client. When the user makes a request, the client queries registered MCP servers to discover available tools. The LLM decides which tool to invoke, the client sends the call to the corresponding MCP server, the server executes the operation, and returns a structured result. This entire flow is controlled within the host's trust boundary — users explicitly choose which MCP servers to connect.
The three core primitives are: Tools (callable functions like run_sql, fetch_url, write_file), Resources (readable data like documents, schemas, configs that the model can use as context), and Prompts (reusable, server-defined prompt templates). The 2024 spec added Sampling, allowing MCP servers to request LLM completions — turning the protocol bidirectional.
MCP has the largest ecosystem of any agent protocol today. Thousands of community-built MCP servers cover Slack, GitHub, Postgres, Notion, Google Drive, Stripe, and virtually every major API surface.
Deep Dive: A2A (Agent-to-Agent Protocol) #
Agent-to-Agent Protocol
A2A defines how autonomous AI agents communicate with each other as peers. One agent can discover another, delegate a task, and receive results — without knowing the internal implementation of the other agent.
- Built on HTTP + JSON + Server-Sent Events (SSE)
- Agent Cards for decentralized capability discovery
- Task lifecycle: submitted → working → completed (or failed, cancelled)
- Direction: horizontal — agents coordinate as peers across boundaries
- Launch partners: Salesforce, SAP, Workday, ServiceNow, Deloitte, KPMG, and 40+ more
How A2A works
Every A2A-compliant agent publishes an Agent Card — a JSON document at the well-known URL /.well-known/agent.json. This card describes the agent's name, capabilities (called skills), supported input/output formats, and the HTTP endpoints to use for communication. No central registry is required; discovery is decentralized by design.
An orchestrator agent fetches a target agent's card, then sends a Task — a structured JSON request describing what work to perform. Long-running tasks stream intermediate updates back via Server-Sent Events. When complete, the agent returns Artifacts — the structured output of the task. For asynchronous workflows, agents can deliver results via push notifications (webhooks) rather than requiring the caller to stay connected.
A2A is explicitly designed to be framework-agnostic and vendor-neutral. An agent built with LangChain can communicate with one built on AutoGen or a proprietary enterprise platform, as long as both implement the A2A spec. This was the gap MCP does not fill — MCP governs a model's relationship with its tools, not how separate agent systems talk to each other.
Deep Dive: ACP (Agent Communication Protocol) #
Agent Communication Protocol
ACP is a REST-based protocol focused on agent interoperability, primarily within the BeeAI framework. It emphasizes simplicity: pure REST, no custom transports, local-first design.
- REST/HTTP native — no SSE required
- Supports synchronous (
/runs) and asynchronous (/runs/async) invocation - Registry-based agent discovery
- Direction: horizontal — similar scope to A2A
- Ecosystem: IBM enterprise, BeeAI framework; earlier adoption stage than A2A
How ACP works
Agents register with a local or federated registry, exposing their capabilities. Callers discover agents through this registry and invoke them via standard HTTP. Synchronous invocations use POST /runs and receive the result immediately. Asynchronous invocations use POST /runs/async, returning a run ID that the caller can use to poll GET /runs/{id}/status for results.
The design philosophy is intentional simplicity: if you can make a standard HTTP request, you can use ACP. There are no special transports, no Agent Cards to publish, and no SSE infrastructure to maintain. This makes ACP easier to integrate with existing REST API tooling — but means discovery is registry-dependent rather than decentralized. ACP is currently most mature within the IBM BeeAI open-source ecosystem and is less widely adopted outside of it.
Side-by-Side Comparison #
| Property | MCP | A2A | ACP |
|---|---|---|---|
| Created by | Anthropic | IBM / BeeAI | |
| Released | Nov 2024 | Apr 2025 | 2025 |
| Primary purpose | LLM ↔ Tools / Data | Agent ↔ Agent | Agent ↔ Agent |
| Communication direction | Vertical | Horizontal | Horizontal |
| Transport | stdio / HTTP+SSE | HTTP + SSE | REST / HTTP |
| Discovery mechanism | None built-in | Agent Cards (decentralized) | Registry-based |
| Streaming | Via SSE (remote) | Yes (SSE) | Polling-based |
| Long-running tasks | Limited | Yes (full lifecycle) | Yes (async pattern) |
| Auth model | OAuth 2.0 / host-controlled | Enterprise auth (pluggable) | Standard HTTP auth |
| Spec maturity | Stable (v1.x) | Active development | Early |
| Ecosystem / adoption | Very wide — thousands of servers | Growing fast — 50+ launch partners | Early stage — BeeAI-centric |
| Best for | Tool & data integration | Enterprise multi-agent systems | IBM / BeeAI stack |
How They Fit Together #
The most important architectural insight: MCP and A2A are not alternatives — they operate at different layers of the same system. A production multi-agent deployment typically uses both.
Orchestrator Agent
│ Decides what to delegate, to whom
└────────────┬─────────────────────────┘
A2A ← agent-to-agent layer (horizontal)
┌────────────┴─────────────────────────┐
Specialist Agent (e.g., "Research Agent")
│ Executes a focused, scoped task
└────────────┬─────────────────────────┘
MCP ← model-to-tool layer (vertical)
┌────────────┴─────────────────────────┐
Tools Web search · Database · File system · APIs
└──────────────────────────────────────┘
A concrete example: a user asks an orchestrator agent to "research recent AI governance regulations and draft a summary." The orchestrator uses A2A to delegate to a specialized research agent. That research agent, internally, uses MCP to call a web search tool, fetch a PDF, and query a vector database. It returns an Artifact to the orchestrator via A2A. The orchestrator then uses MCP to write the final document to the user's file system.
ACP occupies the same horizontal layer as A2A. In practice, a system would use one or the other for agent coordination, not both. ACP is the appropriate choice specifically within IBM's BeeAI ecosystem; A2A is the better default elsewhere, given its broader industry adoption.
The practical architecture: Use MCP for every agent's internal tool connectivity. Use A2A for all agent-to-agent coordination. These two choices cover the full stack for most multi-agent systems.
Choosing the Right Protocol #
Use this when
MCPYour AI model needs to call external tools, read files, query a database, or interact with any API. MCP is the de facto standard for model-to-tool connectivity with the largest available ecosystem of pre-built servers.
Use this when
A2AYou're orchestrating multiple specialized agents that need to coordinate across service or organizational boundaries. A2A's Agent Cards enable decentralized discovery across heterogeneous frameworks (LangChain, AutoGen, proprietary systems).
Use this when
ACPYour team is working within the IBM BeeAI ecosystem, or you need a pure REST design without SSE infrastructure. ACP offers simpler integration for straightforward agent coordination where decentralized discovery is not required.
Use this when
MCP + A2AYou're building a production multi-agent system — this is the emerging standard architecture. MCP handles each agent's tool connectivity internally; A2A handles all inter-agent coordination. Most enterprise deployments will converge on this combination.
If you're starting a new project today: implement MCP immediately for tool access, then add A2A when your architecture grows to include more than one agent. ACP is worth evaluating only if you have a specific dependency on the BeeAI framework or IBM's enterprise stack.
Why This Matters Beyond the Technical #
Protocol standardization is how ecosystems consolidate. The organization whose protocol becomes the default gains structural leverage — every tool, every agent, every enterprise deployment flows through their abstraction layer. This is not a technical curiosity; it is a question of platform power.
We are watching the same dynamics that played out with HTTP, SQL, and REST — but compressed from decades into years. The decisions being made now about which protocols to adopt will shape the architecture of AI systems for the next decade. Teams that understand the protocol stack will design systems that remain interoperable as the ecosystem evolves. Teams that don't will build brittle, vendor-locked integrations that break when the landscape shifts.
This is why protocol literacy matters — not just for engineers making implementation decisions, but for anyone responsible for AI infrastructure strategy.
Frequently Asked Questions #
What is the difference between A2A and MCP?
MCP connects a single LLM to external tools and data sources — it is a vertical protocol that gives a model capabilities. A2A connects autonomous AI agents to each other as peers — it is a horizontal protocol for task delegation between agents across organizational or framework boundaries. They solve different problems: MCP handles what a model can do; A2A handles how separate agent systems collaborate. They are designed to be used together in the same architecture.
Is A2A replacing MCP?
No. A2A and MCP address fundamentally different problems and are not competing. Google explicitly designed A2A to be complementary to MCP, and Anthropic is listed as one of A2A's launch partners while continuing to develop MCP independently. In the canonical multi-agent architecture, each agent uses MCP internally for tool access, and A2A governs all inter-agent communication. Neither protocol makes the other redundant.
Can A2A and MCP be used in the same system?
Yes — and this is the recommended pattern. A2A handles agent-to-agent coordination at the orchestration layer. Each individual agent uses MCP internally to connect to the tools and data it needs to complete its work. The two protocols operate at distinct layers and do not conflict. A typical flow: orchestrator delegates via A2A → specialist agent receives task → specialist calls tools via MCP → returns Artifact to orchestrator via A2A.
What is an Agent Card in A2A?
An Agent Card is a JSON document that an A2A-compliant agent publishes at the well-known URL /.well-known/agent.json on its HTTP server. It describes the agent's name, description, skills (capabilities), supported input and output formats, and the endpoints other agents should use to send tasks. Agent Cards enable decentralized agent discovery — no central registry is required. Any agent can find and communicate with another simply by fetching its Agent Card.
What transports does MCP support?
MCP supports two transports. stdio is used for local processes: the host application and MCP server communicate via standard input/output, suitable for running MCP servers as local processes. HTTP + SSE is used for remote servers: the client sends requests over HTTP and receives streamed responses via Server-Sent Events. Remote MCP servers are appropriate when the tool or data source is hosted as a web service rather than running locally.
How does ACP differ from A2A technically?
Both protocols enable horizontal agent-to-agent communication, but differ in design philosophy. A2A uses HTTP + JSON + SSE and introduces Agent Cards for decentralized, registry-free discovery. ACP is pure REST — no SSE required — with a registry-based discovery model and a polling pattern for async results. A2A has broader industry adoption and enterprise backing. ACP offers a simpler integration surface for teams that find SSE infrastructure burdensome or that work within IBM's BeeAI ecosystem.
Who created MCP, A2A, and ACP?
MCP was created by Anthropic, released as an open standard in November 2024. A2A was created by Google, released as an open protocol in April 2025 with over 50 technology partners including Salesforce, SAP, Workday, and ServiceNow. ACP was created by IBM as part of the BeeAI open-source framework in 2025. All three are open protocols with public specifications and open-source SDKs available.
Which AI agent protocol will become the standard?
MCP has already achieved de facto standard status for model-to-tool connectivity — its ecosystem is vastly larger than any alternative. In the horizontal agent-to-agent layer, A2A has significantly stronger industry momentum than ACP, backed by Google's infrastructure and 50+ enterprise partners at launch. The most likely outcome is MCP + A2A emerging as the canonical two-layer stack for multi-agent systems, with ACP persisting as a relevant option within the IBM/BeeAI ecosystem. This mirrors how HTTP became universal while SOAP remained relevant in specific enterprise contexts.
Does A2A require a central registry?
No. A2A uses a decentralized discovery model based on Agent Cards — each agent publishes its own capabilities at /.well-known/agent.json. An orchestrator discovers an agent simply by fetching that URL. No central directory or registry is needed. This is a deliberate design choice that makes A2A resilient to single points of failure and easier to deploy across organizational boundaries where a shared registry would require coordination. ACP, by contrast, uses a registry-based model.
Is MCP safe to use in production?
MCP is designed with security in mind: the host application controls which MCP servers are registered, users must explicitly authorize connections, and OAuth 2.0 is supported for authentication with external services. The main security consideration is supply chain risk — as with any third-party integration, community-built MCP servers should be reviewed before use. The MCP specification includes guidance on trust levels and permission scoping for both local and remote servers.
Further Reading
- Model Context Protocol — Official Documentation (Anthropic)
- Agent-to-Agent Protocol — Specification and SDKs (Google)
- Agent Communication Protocol — Official Documentation (IBM/BeeAI)
- Protocols Don't Fail. Organizations Deploy Them at the Wrong Level. — a2aix
- AI Governance Map — Research on AI organizational competence