MCP, A2A, ACP, ANP, AG-UI, AP2, UCP and beyond — the new communication standards shaping how AI agents think, talk, and transact. A full breakdown of every protocol, where it fits, and a comparison table of pros and cons.
Three years ago, building an AI agent meant picking a model and writing prompts. Today it means navigating a layered stack of communication standards — protocols that determine how your agent connects to tools, coordinates with other agents, renders results in a UI, and safely handles payments.
The good news: each protocol solves a distinct, well-scoped problem. Once you understand the layers, the landscape stops feeling chaotic and starts feeling like good engineering. This article maps every major protocol, explains what it does, and gives you a comparison table to guide architecture decisions.
The Agent Protocol Stack
What it does: MCP is the USB-C of AI tooling. It defines a standard way for a language model (or any AI client) to discover and call tools — file systems, databases, APIs, code executors — without custom integration code for each.
How it works: A lightweight JSON-RPC 2.0 protocol runs over stdio or HTTP+SSE. The model sends a tools/call request; the MCP server executes it and returns a structured result. Servers can also expose resources (read-only context) and prompts (reusable templates).
Pros: Huge ecosystem (1000+ servers), model-agnostic, simple to implement, backed by Anthropic with Claude native support.
Cons: Designed for single-agent → tool calls; not built for agent-to-agent delegation or stateful multi-turn agent sessions.
What it does: A2A lets one AI agent delegate tasks to another — across vendors, clouds, and frameworks. Each agent publishes an Agent Card (a JSON manifest describing its capabilities), and clients discover and hire agents dynamically.
How it works: Built on HTTP + JSON. An orchestrator agent sends a task to a remote agent endpoint. The remote agent responds synchronously or streams updates via SSE. Agents authenticate via OAuth 2.0 / API keys.
What it does: ACP is IBM's take on agent-to-agent communication, optimised for local and enterprise deployments. Where A2A is cloud-native, ACP is framework-native — it's designed to work inside BeeAI and similar agent runtimes with lower overhead.
How it works: REST API with multipart message payloads. Agents register with a local ACP server. Clients call POST /runs to start a task, poll or stream for results. Supports synchronous, async, and streaming modes.
What it does: ANP takes a decentralised, web-native approach. Rather than a central registry, agents are identified by DIDs (Decentralised Identifiers) and discovered via crawlable JSON-LD manifests — essentially a DNS-style mesh for agents.
How it works: Agents publish a agent.json manifest at a well-known URL. Communication uses standard HTTPS with DID-based authentication. The network is peer-to-peer — no central coordinator.
What it does: AG-UI is the missing link between backend agents and frontend UIs. It defines a standard event stream that any agent can emit, and any UI framework can consume — enabling real-time streaming text, tool call progress, state updates, and shared state synchronisation.
How it works: The agent emits typed events (TEXT_MESSAGE_CHUNK, TOOL_CALL_START, STATE_DELTA, etc.) over SSE or WebSocket. The frontend SDK subscribes and renders them. Works alongside MCP and A2A — it's purely about the UI layer.
What it does: AP2 defines how agents request, authorise, and execute payments — without a human in the loop. An agent can purchase API credits, pay for services from other agents, or settle micro-transactions autonomously, within pre-approved spending limits.
How it works: Payment requests include a structured PaymentIntent with amount, currency, purpose, and recipient. The human pre-approves a budget and delegates authority. The agent signs and submits the intent; the payment provider settles it.
What it does: UCP goes beyond payments to cover the full commerce lifecycle for agents: product discovery, negotiation, purchase, fulfilment tracking, and returns — all without human intervention. Think of it as an EDI system redesigned for AI agents.
How it works: Structured JSON messages cover each commerce stage. Sellers publish an agent-commerce.json manifest; buyers send structured purchase requests. Smart contracts or escrow services can optionally enforce terms.
What it does: X42 is an experimental protocol for long-horizon agent execution — tasks that span hours or days, survive restarts, and require checkpoint/resume semantics. It addresses the durability gap in current agentic frameworks.
What it does: W3C's working group on agent-accessible web is defining how websites should publish structured capability metadata — so agents can reliably understand what actions are available (search, checkout, book) without scraping.
| Protocol | Layer | By | Transport | Best For | Maturity |
|---|---|---|---|---|---|
| MCP | Tool Access | Anthropic | stdio / HTTP+SSE | Connecting agents to tools & data | ✅ Production |
| A2A | Agent ↔ Agent | HTTP + SSE | Cross-vendor agent orchestration | ✅ Production | |
| ACP | Agent ↔ Agent | IBM | REST | Enterprise & local deployments | 🟡 Beta |
| ANP | Agent ↔ Agent | Community | HTTPS + DIDs | Open internet agent mesh | 🟡 Early |
| AG-UI | Agent ↔ UI | CopilotKit | SSE / WebSocket | Streaming agent UIs | ✅ Production |
| AP2 | Commerce | Community | HTTP | Autonomous payments | 🔴 Experimental |
| UCP | Commerce | Emerging | HTTP / Smart contracts | Full agentic commerce | 🔴 Experimental |
| X42 | Execution | Research | Various | Long-running durable tasks | 🔴 Research |
| W3C WACG | Web Access | W3C | JSON-LD | Structured web capabilities | 🟡 Draft |
These protocols are complementary, not competing. A production agent system in 2026 might use all of them simultaneously:
The layer model is the mental model. Once you see each protocol as solving one layer, the choices become obvious.