Understanding the AI Agent Protocol Stack: MCP, A2A, AG-UI, A2UI, AP2, and UCP

1. Protocol Stack Logic: 60‑Second Overview

The AI Agent protocol stack can be likened to the TCP/IP network stack: each layer has a distinct responsibility and there is no redundancy or overlap between layers.

2. MCP (Model Context Protocol): Connection Layer Between Agent and Tools

Anthropic released MCP at the end of 2024 to solve the fragmentation of external‑resource connections in Agent development.

Before MCP, every integration required custom code; each Agent that needed to access the same database had to re‑implement the integration.

MCP is the USB‑C of the AI field. It provides a standardized connector. Developers build an MCP Server for a database, and any MCP‑compatible Agent can call it directly. Conversely, an Agent implements an MCP Client to access all MCP Servers, making MCP the foundational infrastructure for any external‑resource interaction.

3. A2A (Agent to Agent): Collaboration Layer

Google released A2A in April 2025 and donated it to the Linux Foundation in June 2025. IBM subsequently stopped development of the competing ACP protocol and merged it into A2A. The standard now has backing from more than a hundred companies, including AWS, Microsoft, Cisco, and Salesforce.

The core concept of A2A is the "Agent Card" , a JSON‑formatted declaration that publicly lists an Agent’s capabilities and endpoints—essentially a machine‑readable business card. Using A2A, an Agent can discover another Agent’s abilities and orchestrate task delegation and result collection.

Use case: When a monolithic Agent cannot handle a complex workflow, A2A enables specialized Agents to collaborate and form a delegation chain.

4. AG-UI: Synchronization Layer Between Agent and Frontend

CopilotKit released AG‑UI in May 2025 to bridge the experience gap between Agent demos and mature products such as Cursor.

Core conflict: Agent execution involves token streaming, mid‑execution tool calls, human approvals, and long‑dialogue state maintenance. Traditional REST APIs cannot natively handle these requirements, forcing developers to rely on complex WebSocket wrappers or inefficient polling.

AG‑UI standardizes this process. It allows the frontend to perceive the Agent’s thinking state, tool‑call progress, approval pauses, and status updates in real time. Whenever a human user interacts with an Agent through a UI, AG‑UI is the required layer.

5. A2UI: Agent‑Generated Interaction Layer

Google released A2UI in December 2025 to answer the basic question: why can an Agent only return plain text?

In real business scenarios, users often need forms, charts, confirmation dialogs, or product comparison cards. A2UI enables an Agent to generate structured UI directly. The Agent outputs a standard format, and the frontend renders it. If AG‑UI is the transport pipe, A2UI is the standardized cargo flowing through that pipe.

6. AP2 (Agent Payments): Payment Layer

Google launched AP2 in September 2025 together with Mastercard, PayPal, Stripe and more than 60 partners.

Engineering challenge: Traditional payment flows assume a human as the actor. When an Agent initiates a purchase, the system must prove the legitimacy of the authorization, handle disputes, and define liability.

AP2 introduces a "Mandates" mechanism—cryptographic signatures that prove user intent. It includes an Intent Mandate, a Cart Mandate, and a Payment Mandate, each providing cryptographic evidence for its step.

7. UCP (Agent Commerce): Business Layer

UCP is the newest layer, jointly released by Google and Shopify in January 2026 and already supported by retail giants such as Walmart and Target.

Pain point: The commerce environment is highly fragmented. Different retailers expose distinct search, inventory, cart, and checkout APIs, leading to massive integration costs for shopping Agents.

UCP abstracts commercial logic. An Agent discovers a merchant’s capabilities via a Profile and handles checkout, inventory, and after‑sale operations through a unified interface. UCP can run on top of REST, MCP, or A2A and leverages AP2 to ensure secure payments.

8. Stack Collaboration in Practice

Example shopping scenario: a user asks an Agent to find running shoes under $150.

MCP: Extracts the user’s size preference and purchase history.

A2A: Coordinates a price‑comparison Agent and an inventory Agent.

UCP: Queries real‑time stock and pricing from various merchants.

AG-UI: Streams execution status to the user’s device.

A2UI: Generates comparison cards for the user’s decision.

AP2: After user confirmation, creates a cryptographic signature to complete payment.

9. Engineering Selection Recommendations

Developers should introduce layers according to system complexity:

Basic tool integration: Use only MCP. This covers about 80 % of fundamental use cases.

Multi‑Agent collaboration: Add A2A. Each Agent manages its tools via MCP internally and collaborates externally via A2A.

UI interaction optimization: Add AG‑UI. It handles streaming state synchronization and human‑in‑the‑loop approvals.

Rich interactive output: Add A2UI. It enables dynamic components instead of plain text.

Financial/commercial scenarios: Add AP2 and UCP to avoid reinventing payment authorization and platform integration.

10. Conclusion

One year ago the Agent ecosystem was chaotic. The current trend is hierarchical convergence. Major players have reached consensus to build around complementary layers rather than competing standards.

Future protocols will aim to fill gaps in the stack instead of duplicating existing layer functionality. Hierarchical layers, not competition, signal the engineering maturity of AI Agents.