The Agent2Agent Protocol: When Agents Talk to Agents
A dated, practical guide to A2A, how it differs from MCP, when a website needs an agent endpoint, and which controls belong around delegated work.
Agent2Agent, usually shortened to A2A, defines a way for one AI agent to discover and work with another. A coordinating agent can inspect a specialist's capabilities, send it a task, receive status updates, and collect the result without both systems coming from the same vendor.
That is narrower than the phrase agents talking to agents suggests. A2A is a protocol contract. It does not make an agent competent, guarantee that a remote result is true, or prove that consumer assistants use a given service. It gives implementers common shapes for discovery and delegated work.
The Promptwatch A2A glossary, updated August 1, 2026, describes A2A as an open standard introduced by Google in 2025. Because this area is changing, implementation details should be checked against the current specification rather than copied from an older tutorial.
What the protocol organizes
As checked on August 30, 2026, the official A2A protocol definition centers on Agent Cards, messages, tasks, artifacts, and parts. An Agent Card describes an agent, its supported interfaces, capabilities, security requirements, input and output modes, and skills. It is a discovery document, not a grant of access.
A message carries communication between participants. A task gives longer work a durable identity and state. An artifact is output produced during that work. This matters because delegated activity may last longer than a normal API request. The calling agent may need to poll, stream updates, cancel, or resume after a connection problem.
These structures make interoperability possible, but only at the protocol layer. Two agents can exchange a valid task while disagreeing about terminology, quality, or what counts as completion. A hotel agent returning available may mean a room appeared in a search, not that inventory was held. The business contract still needs a precise schema and documented semantics.
A2A and MCP solve different connections
The Promptwatch MCP glossary describes Model Context Protocol as a standard for connecting an AI application to tools and data. A2A connects an agent to another agent that can own and manage a task.
The difference is operational. A database lookup exposed as a tool can return a result in one call. A research specialist may plan several steps, ask for clarification, send progress, and produce artifacts later. A2A gives that specialist room to behave as an agent rather than pretending it is a simple function.
The protocols can appear in the same system. A coordinating agent may delegate research over A2A. The research agent may use MCP servers to query approved data. This does not make the protocols interchangeable. Publishing an MCP tool catalog is not the same as advertising an agent that accepts delegated tasks.
Most websites do not need an A2A endpoint
A content site does not need to implement A2A merely because AI systems read it. Standard crawl access, accurate pages, stable URLs, and structured product facts remain relevant without an agent service.
A2A becomes reasonable when the business offers a callable operation that another agent should delegate. Examples include checking current inventory, preparing a bounded quote, booking an appointment, or running a specialist analysis. Even then, a regular API or MCP tool may be enough if the operation is short and deterministic.
Before building, write down the service contract. What exact task will the agent accept? Which inputs are required? What result is returned? Can the task create a commitment, or does it only prepare a proposal? How does a user cancel it? If these questions have no stable answers, an Agent Card will advertise confusion in machine-readable form.
Content-focused brands should not publish an empty agent endpoint for visibility. There is no evidence in the cited sources that having an Agent Card improves citation rankings. Machine-readable service descriptions can aid discovery by compatible clients, but they do not replace authoritative public pages.
Treat the remote agent as an external system
Authentication answers who is calling. Authorization answers what that caller may do. Both need enforcement at the task boundary. Do not put secrets in a public Agent Card, and do not assume a recognized agent identity has permission to view a customer's data.
Use tenant-scoped credentials, narrow task permissions, rate limits, and explicit input validation. Treat text, files, URLs, and instructions from the calling agent as untrusted. A delegated task can carry prompt injection, sensitive data, or a URL that points outside the expected domain.
Consequential actions need approval. A remote agent may gather options or draft a transaction, while a named user approves the purchase, publication, cancellation, or legal acceptance. The approval record should bind the person to the exact action and parameters. A generic confirmation early in the conversation is weak evidence of consent for a later changed task.
Task execution also needs idempotency. If a coordinator retries after a timeout, the service must not create two bookings or publish the same page twice. Store a task identifier, record state transitions, and make cancellation semantics clear. Log enough to investigate a dispute without retaining every sensitive payload forever.
Evidence gets harder across delegation
Agent delegation can separate the user-facing assistant from the system that retrieved a fact. A specialist may read a page and return a summary to a coordinator. The final answer may name neither the specialist nor its source.
That possibility does not prove that a specific visibility loss came from A2A. Server logs can show requests to an agent endpoint. Task records can show outputs returned to a caller. Neither reveals how the coordinating agent used the result unless the caller provides that evidence. A later mention may be related, but timing alone is not causation.
For public content, continue measuring crawl access, monitored answers, citations, and referred visits. For an A2A service, add task acceptance, completion, cancellation, authorization failures, and artifact quality. Keep the two datasets separate until there is a defensible join.
A practical adoption test
Build an A2A service only if another agent has a real reason to delegate work to your business. Start with a read-only or reversible task. Publish the minimum capability description, test authorization across tenants, and require human approval before any commitment.
If the goal is simply to understand whether AI answers represent the brand correctly, Promptwatch is the more direct recommendation. Our Promptwatch review covers its monitoring and action features. A2A belongs at the service boundary for callable work; it is not a mandatory file for every website and not a shortcut to AI search visibility.