What is Agent2Agent (A2A)? How AI agents communicate and collaborate – Unite.AI

Agent2Agent (A2A) is an open protocol that allows AI agents to discover each other, exchange messages, delegate tasks, report progress, and return results across system boundaries. It is designed for situations where one agent needs help from another without requiring either party to expose their internal reasoning, memory, or implementation.

As organizations employ specialized agents, communication becomes an infrastructure problem. A contracting agent may need information from a compliance agent; a customer service agent may need a logistics agent to investigate a shipment. A2A provides a common way to coordinate that work even when agents use different frameworks, vendors, or templates.

Why agents need a communication standard

Traditional APIs expose functions and data, but an agent-to-agent interaction can be more open. The receiving agent may have to interpret a goal, decide how to solve it, ask follow-up questions, work for minutes or hours, stream updates, and return various artifacts.

Without a shared protocol, each platform would define its own formats for identity, capability detection, activity, messages, status, and errors. This fragmentation makes cross-platform delegation difficult and locks out useful agents within individual products.

A2A standardizes the communication layer by allowing each agent to remain a black box. The current A2A specification defines the main objects and interactions of the protocol.

The key roles in A2A

This separation is what makes A2A different from a simple function call. The remote participant can handle a long-running task, request additional information, negotiate supported content types, and return one or more items. The client agent tracks activity while preserving the identity and authority of the user or application that initiated it.

An A2A interaction usually involves two logical roles:

• Customer Agent: the agent or application requesting work.
• Remote Agent: the agent who receives the request and performs or coordinates the work.

The words “customer” and “remote” describe the current interaction, not a permanent hierarchy. The same agent can request work in one context and serve another agent in a different context.

Agent Cards: Discovering Abilities

Before delegating a task, a client must know what a remote agent can do and how to communicate with it. A2A uses an Agent Card to publish descriptive and operational metadata.

An agent card can describe an agent’s name, endpoint, supported protocol capabilities, authentication expectations, skills, and accepted content types. A skill is a stated area of ​​ability, such as translating a document, verifying a contract, or researching a market.

The discovery does not demonstrate quality or reliability. An Agent Card is a statement of capability, not an independent certification. Production systems still require identity, authorization, policy controls, reputation and evaluation.

Messages, activities and artifacts

A2A represents collaboration through several fundamental objects.

Messages

Messages carry communication between agents. They can contain text and other structured parts, allowing agents to exchange instructions, clarifications or contextual material.

Assignments

An activity represents a unit of work whose state can change over time. A remote agent can accept the job, continue processing, request additional input, complete it, fail it, or cancel it. Persistent task identity is useful for long-running operations because the client can refer to the same process between updates.

Artifacts

Artifacts are the outputs produced by the work, such as a report, a dataset, an image, a code patch, or a structured recommendation. Separating artifacts from conversational messages makes it easier for the customer to identify and consume the deliverables.

How an A2A interaction works

Suppose a travel planning agent needs a specialist to check entry requirements.

1. The client detects a remote agent and reads its agent card.
2. Verify that the agent advertises the relevant functionality and a compatible interaction method.
3. The client authenticates and sends a message describing the activity, travelers, dates, and the requested output.
4. The remote agent creates or updates a task and starts working.
5. The remote agent can stream the progress or request a missing detail.
6. The client provides clarification while preserving the context of the task.
7. The remote agent completes the task and returns a structured artifact with its result.
8. The customer evaluates that result before using it in the larger travel plan.

The remote agent decides how to carry out its task. It could summon its own tools, consult private data, or coordinate additional agents. A2A does not require such internal passages to be disclosed.

A2A against MCP

Protocols can be on different layers of the same architecture. A travel planning agent might delegate a specialized visa research task to A2A. The specialist agent could then use MCP connections to search approved databases and retrieve policy documents. A2A coordinates responsibility among agents; MCP standardizes access between a host and AI capabilities.

A2A and the Model Context Protocol solve several integration problems.

• MCP connects an AI application to tools and context. A client discovers capabilities such as functions, resources, and prompts from an MCP server.
• A2A connects agents to agents. A client delegates a goal-oriented task to a remote agent that can manage its process and return a result.

The difference resembles using a tool versus hiring a specialist. A calculator exposes an operation; an analyst accepts a goal and decides what operations are necessary. In real systems, a remote A2A agent can use MCP internally to reach its tools and data.

A2A and ordinary APIs

A conventional API is ideal when the caller knows the exact operation and input format: retrieve a record, calculate a quote, or update a field. A2A is useful when the request is conversational, stateful, asynchronous, or results-oriented.

A2A does not replace all APIs. Remote agents often call ordinary APIs to do their work, and organizations can directly expose deterministic services when agent discretion adds no value.

Why interoperability matters

Agent ecosystems will be heterogeneous. Different teams will optimize for different domains, models, security boundaries, and deployment environments. A shared protocol allows organizations to preserve that specialization while enabling collaboration.

Interoperability can also reduce integration coupling. A client can depend on a declared skill and protocol behavior instead of importing the remote agent’s structure or duplicating its internal logic. The A2A project overview describes this goal as allowing agents built on different stacks to communicate as peers; The 2026 blueprint update on Agentic AI Foundation membership reflects the push towards neutral, cross-sector governance.

Security and trust challenges

Delegation creates a chain of responsibility. The client must verify the remote agent’s identity and advertised capability, minimize shared context, and preserve the authorization of the user who initiated the operation. The remote agent should not inherit broad privileges simply because another agent requested the task. Each hop needs authentication, scoped credentials, auditability, and a clear rule about what happens when requirements conflict or confidence is low.

Agent-to-agent delegation creates a chain of authority. A client may accidentally share sensitive context, give a remote agent more discretion than expected, or act on an unreliable artifact. The remote agent can also receive malicious instructions or files from an untrusted client.

Effective implementations require controls at multiple levels:

• Identity and authentication: check which agent and organization participates.
• Authorization: limit the skills, data, actions and scope of activities available to each caller.
• Data Minimization: share only the context the remote agent needs.
• Origin: record who requested the work, which agent produced it, and which sources support it.
• Output validation: treat remote artifacts as untrusted until they pass the relevant checks.
• Delegation limits: check whether a remote agent can involve additional agents or services.
• Human Approval: pause before financial, legal, external, destructive, or otherwise consequential actions.

Protocol compatibility does not imply organizational trust. An agent can speak A2A correctly and still be inappropriate for a particular task.

When should teams use A2A?

A2A is particularly attractive when independent agents need to collaborate across product, supplier, or organizational boundaries; when the work is long lasting; or when the receiving system should retain freedom in how to produce the result.

It may not be necessary for a simple function, a fixed internal workflow, or tightly coupled components within an application. In these cases, an ordinary API, event bus, or direct tool call may be easier to use and evaluate.

What to remember about what Agent2Agent (A2A) is

A2A provides a common language for agents to discover capabilities and coordinate goal-directed work without sharing their internal mechanisms. Its core value is not that multiple agents are automatically better than one, but that independently trained specialists can collaborate across a stable boundary.

That border must carry more than just messages. Identity, task state, artifacts, permissions, provenance, and error handling are needed. A2A provides the basis of the protocol; organizations continue to provide the trust model.