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A2A Explained: What Happens When AI Agents Start Talking to Other Agents?
AI agents are moving beyond answering questions.
An agent may now research information, use software tools, interact with enterprise systems, monitor operations, make decisions, and coordinate workflows. But a new problem appears as soon as organizations deploy more than one agent:
How does one AI agent reliably work with another agent it did not build?
A sales agent may need a pricing agent. A procurement agent may need an inventory agent. An industrial monitoring agent may need a maintenance agent. If every connection requires a custom integration, multi-agent systems quickly become difficult to maintain.
The Agent-to-Agent (A2A) Protocol addresses this interoperability problem by defining a common way for independent agents to discover capabilities, exchange messages, delegate work, track tasks, and return results.
Here is what actually happens when AI agents start talking to other agents.
What Is the Agent-to-Agent A2A Protocol?
The Agent-to-Agent (A2A) Protocol, also written as Agent2Agent, is an open standard for communication and collaboration between independent AI agent systems.
Google originally introduced A2A in April 2025. The project was subsequently donated to the Linux Foundation, and A2A v1.0 was announced in March 2026 as the first stable production-ready version of the protocol.
The basic idea is simple.
Imagine two agents:
Agent A: A customer service agent.
Agent B: A logistics agent.
A customer asks:
"Where is my replacement order, and can you change the delivery address?"
The customer service agent may understand the request but not control logistics systems.
Instead of pretending to know the answer or embedding every logistics capability inside itself, Agent A can discover Agent B, determine what it can do, send it a structured request, receive updates, and use the returned result to continue helping the customer.
A2A provides the standardized communication layer for that interaction.
Importantly, Agent B does not need to expose its internal reasoning, model, memory, tools, or implementation to Agent A.
The official specification describes A2A as a mechanism for independent and potentially opaque agent systems to collaborate while remaining internally independent.
Why A2A matters
Without interoperability, organizations risk building isolated agent ecosystems.
One team creates an agent in Python.
Another uses Java.
A SaaS provider exposes another agent.
A partner runs a different agent framework.
A cloud platform introduces its own agent runtime.
If every combination requires custom glue code, the architecture becomes:
Agent A → Custom Connector → Agent B
Agent A → Another Connector → Agent C
Agent B → Different Connector → Agent D
As the number of agents grows, the number of integration paths can grow quickly.
A2A attempts to replace that pattern with a shared contract:
Agent → A2A → Agent
The protocol is designed specifically for agents built using different technologies, frameworks, and vendors.
For organizations experimenting with multi-agent AI, the important question is therefore becoming less about "Which agent framework should we choose?" and more about "How will independently developed agents interoperate safely?"
If your organization is designing an agentic AI architecture and interoperability is becoming a concern, Infolitz can help review the integration boundaries before they turn into custom dependencies.
How Does the A2A Protocol Work?
The easiest mental model is to think about six stages:
Discovery → Selection → Message → Task → Progress → Result
Let's walk through them.
1. The agent publishes an Agent Card
Before one agent can collaborate with another, it needs to understand what the other agent can do.
A2A uses an Agent Card for this purpose.
An Agent Card is a structured JSON document describing information such as:
- Agent identity
- Description
- Available skills
- Service interfaces
- Authentication requirements
- Supported capabilities
- Input and output expectations
The official A2A documentation describes it essentially as the agent's digital business card.
Suppose a logistics agent advertises capabilities such as:
- Check shipment status
- Estimate delivery time
- Request delivery changes
- Create return shipment
Another agent can inspect this information before deciding whether the logistics agent is suitable.
This is important because agent interoperability requires more than merely knowing an endpoint.
The calling agent needs to understand:
"Can this agent actually perform the task I need?"
2. One agent discovers another agent
The requesting agent, sometimes called the client agent, obtains the Agent Card and examines the available skills and communication options.
Conceptually:
Client Agent → Discover Agent Card → Evaluate Skills → Choose Agent
This creates a major difference between traditional hard-coded integration and more dynamic agent architectures.
Traditional software typically knows exactly which service it intends to call.
An agentic system may instead identify a suitable capability during workflow execution.
That opens powerful possibilities, but it also introduces governance questions around which agents may be discovered and trusted.
3. The client agent sends a message
Once an appropriate remote agent has been selected, the client sends a message.
A2A messages represent communication turns between agents.
Messages can contain different content types using Parts, allowing the interaction model to support more than plain text.
Conceptually:
Client Agent
"Check inventory availability for product X across European warehouses."
↓
Inventory Agent
"I can process that request."
The communication remains structured even though natural language may form part of the content.
4. The remote agent creates or processes a task
Some requests can be answered immediately.
Others require longer-running work.
That is why Task is a key concept in A2A.
A task gives the interaction a lifecycle rather than assuming every agent call behaves like an instant API response.
For example:
submitted → working → completed
A complex task might involve:
- Querying multiple inventory systems.
- Checking reservations.
- Calculating available-to-promise inventory.
- Resolving warehouse restrictions.
- Returning a structured result.
The calling agent does not need to understand those internal implementation details.
It tracks the task.
A2A v1.0 defines operations for sending messages, obtaining task information, listing tasks, canceling tasks, subscribing to task updates, and configuring push notifications.
5. The agents exchange progress
Some agent jobs may take seconds.
Others may take minutes or longer.
A useful agent communication protocol therefore needs asynchronous behavior.
A2A supports mechanisms for agents to receive task updates rather than treating every interaction as one blocking request.
For example:
Procurement Agent
"Find three approved suppliers able to deliver this component within seven days."
The sourcing agent could report:
Working: Searching approved suppliers.
Working: Checking availability.
Working: Comparing delivery windows.
Completed: Three eligible suppliers found.
This is particularly important for enterprise workflows where agents interact with slow external systems or perform multi-step operations.
6. The agent returns an artifact
The output of agent work may be represented as an Artifact.
An artifact could represent something such as:
- A generated report
- Structured data
- A document
- A calculation
- A file
- A workflow result
That means the response does not need to be merely conversational.
One agent can delegate useful work to another agent and receive a machine-consumable result.
The full model looks like this:
User Request
↓
Client Agent
↓
Agent Discovery
↓
Agent Card
↓
Remote Agent
↓
Message
↓
Task Execution
↓
Status Updates
↓
Artifact / Result
↓
Client Agent
↓
User or Next Agent
That is the core A2A mental model.
A2A Architecture and Stack Options
A2A should not be confused with an AI model or agent framework.
It is an interoperability protocol that sits between independently operating agents.
A practical architecture could look like:
User / Application
↓
Primary Agent
↓
Agent Orchestration Layer
↓
A2A Communication
↓
Specialized Agents
↓
Tools, APIs, Databases, Enterprise Systems
A specialized agent may itself use:
- Large language models
- APIs
- Databases
- Retrieval systems
- MCP servers
- Enterprise applications
- IoT platforms
- Internal business logic
A2A does not dictate how those internal capabilities must be implemented.
Standard protocol bindings
Current A2A documentation defines standard bindings including:
- JSON-RPC
- gRPC
- HTTP+JSON/REST
The specification also provides a path for custom protocol bindings where specialized environments require them.
This matters because agent-to-agent communication will not happen in one uniform infrastructure.
A cloud-native enterprise system may favor HTTP or gRPC.
An edge or IoT environment could eventually require different communication characteristics.
The protocol separates the agent collaboration model from the underlying implementation sufficiently to support these deployment variations.
A2A SDK options
The current A2A roadmap lists project SDKs for:
- Python
- JavaScript
- Java
- Go
- .NET
- Rust
That makes cross-language agent architectures increasingly practical.
Best Practices for Implementing A2A
A2A solves the communication contract. It does not automatically solve architecture quality.
Several engineering practices still matter.
Treat Agent Cards as contracts
Do not expose vague skills such as:
"Can help with business operations."
Use narrow and understandable capabilities.
A good Agent Card should help another system determine whether the agent is appropriate before sending work.
Keep agents specialized
A network of giant agents that all perform the same operations creates ambiguity.
Clear ownership works better.
For example:
- Customer Support Agent
- Pricing Agent
- Inventory Agent
- Shipping Agent
- Returns Agent
Each should own defined responsibilities.
Design failure states
Agent communication can fail.
A remote agent may be:
- Unavailable
- Unauthorized
- Slow
- Incompatible
- Unable to complete the task
- Returning insufficient information
Design those states explicitly.
Validate outputs
Do not assume that because another AI agent produced a response, that response is valid.
Validate structured outputs against schemas and business rules before allowing downstream automation.
Limit agent permissions
An agent that needs to check inventory should not automatically receive permission to modify pricing.
Use least-privilege access.
Preserve observability
Log enough information to answer:
- Which agent initiated the request?
- Which remote agent handled it?
- What task was created?
- What systems did it access?
- How long did execution take?
- What was returned?
- Did a human approve sensitive actions?
Without this, debugging multi-agent systems becomes difficult very quickly.
Common A2A Pitfalls
Several architectural mistakes are likely to appear as teams adopt agent interoperability.
Making every workflow agentic
Not every integration needs A2A.
If Service A always calls Service B using a deterministic request and response, a normal API may remain simpler.
Use agent collaboration where autonomy, capability discovery, delegation, or multi-step execution creates real value.
Trusting any discoverable agent
Discovery should not mean automatic trust.
Organizations need approved agent registries, identity controls, permission boundaries, and governance.
Sending excessive context
Giving remote agents entire conversation histories or unrestricted business information increases security risk and token cost.
Share only the context needed to perform the task.
Ignoring idempotency
Retries happen.
A repeated query may be harmless.
A repeated purchase order is not.
Agent systems that trigger actions need idempotent or otherwise protected operations.
Hiding deterministic logic inside agents
Use normal software when the outcome should always follow a strict rule.
Use agents when reasoning, interpretation, delegation, or flexible execution is necessary.
The strongest architecture usually combines both.
Performance, Cost, and Security Considerations
Multi-agent architecture adds coordination overhead.
One user request may trigger:
User → Agent A → Agent B → Tool → Agent C → Database → Agent A
Every hop can add latency.
Performance
Track:
- Agent discovery time
- Network latency
- Model inference latency
- External tool latency
- Task execution time
- Serialization overhead
- Retry behavior
Parallel agent execution may improve some workflows, but unnecessary delegation can make simple tasks slower.
Cost
One user request may invoke several models.
For example:
1 request
→ planning agent
→ research agent
→ analysis agent
→ verification agent
→ summarization agent
That means model inference costs can multiply.
Track cost at the task and workflow level, not just per model call.
Security
A2A relies heavily on established web security practices rather than inventing a completely separate identity system.
Current specification guidance requires encrypted production communication and supports established authentication approaches including OAuth 2.0, OpenID Connect, API keys, HTTP authentication, and mutual TLS representations through its security model.
Important controls include:
- TLS
- Authentication
- Authorization
- Least privilege
- Agent identity verification
- Input validation
- Output validation
- Audit logging
- Secret management
- Rate limiting
- Data classification
A2A v1.0 also includes support for Agent Card signatures in its protocol model, strengthening the mechanisms available around agent identity and published metadata.
The larger lesson is simple:
Agent interoperability increases the importance of identity.
When software begins delegating tasks autonomously, systems must know not only what an agent can do, but whether that agent should be trusted to do it.
If you are evaluating multi-agent architecture for production use, Infolitz can help map agent boundaries, integrations, cloud infrastructure, and security controls before implementation.
Real-World A2A Use Cases
Enterprise IT support
Imagine an employee saying:
"My laptop is overheating, VPN keeps disconnecting, and I need a replacement if this cannot be fixed today."
A primary IT agent could delegate work to:
Device Agent
Retrieve hardware telemetry.
Network Agent
Analyze VPN connectivity.
Asset Agent
Check replacement inventory.
Approval Agent
Verify replacement eligibility.
The primary agent coordinates the workflow rather than containing every capability itself.
Supply chain management
A supply chain agent receives:
"Can we deliver 4,000 units to Germany by Friday?"
It may contact:
Inventory Agent
↓
Production Agent
↓
Logistics Agent
↓
Compliance Agent
Each specialist contributes part of the answer.
Software engineering
A development agent discovers a security review agent.
The development agent submits a change.
The security agent analyzes dependencies, configuration, and code.
It returns findings as an artifact.
The development agent then determines whether remediation is required.
IoT operations
A connected-device platform detects abnormal vibration in an industrial motor.
Monitoring Agent
detects anomaly
↓
Diagnostic Agent
analyzes telemetry
↓
Maintenance Agent
checks service history
↓
Inventory Agent
checks spare parts
↓
Scheduling Agent
proposes technician visit
This illustrates why A2A may become relevant beyond purely conversational AI.
IoT environments already consist of distributed systems. Agent interoperability can add a decision and coordination layer above those systems.
A2A vs MCP
One of the most common questions is whether A2A replaces the Model Context Protocol (MCP).
It does not.
The two protocols address different integration problems.
A useful mental model is:
MCP: Agent ↔ Tools and Data
A2A: Agent ↔ Agent
MCP provides mechanisms for AI applications to access capabilities such as tools, prompts, and resources. The current MCP documentation describes tools, resources, and prompts as core server primitives.
A2A focuses on agents discovering and collaborating with other agents.
The A2A project's own documentation describes MCP as a vertical integration layer connecting agents to internal tools and data, while A2A provides a horizontal collaboration layer between agents.
These technologies can therefore work together.
Consider:
User
↓
Travel Agent
↓
MCP → Calendar
MCP → Customer Database
↓
A2A → Flight Agent
↓
A2A → Hotel Agent
↓
A2A → Expense Agent
↓
Final Travel Plan
MCP connects agents to capabilities.
A2A connects agents to other autonomous systems.
In many enterprise architectures, both may appear in the same solution.
A2A vs Traditional APIs
A2A also does not eliminate REST APIs, gRPC services, or event-driven systems.
Traditional APIs remain excellent for deterministic service interactions.
Use a traditional API when the calling system knows:
- Exactly which service to call
- Exactly what operation to invoke
- Exactly what schema to send
- Exactly what response to expect
Agent-to-agent protocols become more useful when the interaction involves:
- Capability discovery
- Delegation
- Long-running tasks
- Agent autonomy
- Multi-turn collaboration
- Dynamic agent ecosystems
- Cross-framework interoperability
In other words:
APIs connect software functions.
A2A coordinates autonomous software actors.
The boundary will sometimes overlap, but the architectural intent is different.
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Frequently Asked Questions
What is the Agent-to-Agent A2A Protocol?
The Agent-to-Agent Protocol is an open standard that lets independent AI agents discover capabilities, exchange messages, delegate tasks, receive status updates, and collaborate across frameworks and vendors.
Who created the A2A Protocol?
Google originally announced A2A in April 2025. The project was later contributed to the Linux Foundation and has since developed under open governance.
What is A2A v1.0?
A2A v1.0 is the stable production-ready protocol release announced in March 2026. It standardized areas including protocol bindings, version negotiation, task operations, Agent Cards, security models, streaming, and cross-platform interoperability.
What is an Agent Card?
An Agent Card is structured metadata describing an agent's identity, capabilities, skills, service interfaces, security requirements, and other information that clients can use to determine how to interact with it.
What is the difference between MCP and A2A?
MCP primarily connects AI applications with tools, resources, and external context. A2A connects independent agents with other agents.
They are complementary technologies rather than direct replacements for one another.
Does A2A replace APIs?
No. APIs remain appropriate for deterministic service-to-service integrations. A2A adds an interaction model specifically designed for autonomous agent discovery, task delegation, collaboration, and task lifecycle management.
Is A2A only for Google agents?
No. A2A is an open standard intended to support agents built with different frameworks, languages, models, and vendors.
Is A2A secure?
A2A provides a protocol model designed to work with standard enterprise security practices including encrypted transport, authentication, and authorization. Production security still depends on how organizations configure identities, permissions, credentials, validation, logging, and infrastructure.
Can A2A work with MCP?
Yes. An agent can use MCP to access tools and information while using A2A to communicate with another agent.
Can A2A be used with IoT?
Potentially, yes. Agents can sit above IoT platforms to coordinate monitoring, diagnostics, maintenance, fleet management, and operational systems. The A2A project also supports custom binding mechanisms, although architects should still evaluate latency, connectivity, compute, and security requirements carefully for edge environments.
What Happens When AI Agents Really Start Talking?
A2A is not mainly about creating AI conversations between bots.
Its deeper purpose is interoperability.
Enterprise AI will increasingly consist of specialized agents operating across different platforms, teams, clouds, vendors, and business systems. Without a shared communication model, organizations risk rebuilding the same integration problem that APIs solved for traditional software.
A2A introduces a common way for agents to say:
Here is what I can do.
Here is how you can communicate with me.
Here is the task I need you to perform.
Here is its current status.
Here is the result.
That sounds simple.
But standardized communication between autonomous systems could become one of the foundational layers of agentic software architecture.
As of September 2026, A2A v1.0 is the stable protocol generation, while the project's published roadmap is already exploring areas such as richer bidirectional streaming, multi-turn workflows, stronger validation tooling, and v1.1 improvements.
The important question for engineering leaders is therefore no longer just:
"Can we build an AI agent?"
It is increasingly:
"How will that agent safely collaborate with everything else?"
The next phase of AI is not just smarter agents. It is agents that can discover, understand, and work with each other.
Conclusion
The Agent-to-Agent (A2A) Protocol addresses a problem that becomes more important as organizations move from individual AI assistants to networks of specialized agents.
Instead of requiring every agent-to-agent interaction to be built as a custom integration, A2A provides a common model for discovering capabilities, exchanging messages, delegating tasks, tracking progress, and returning results.
The bigger architectural shift is not simply that AI agents can “talk.” It is that independently developed agents can begin working together while keeping their own models, tools, logic, and internal systems separate.
A2A also does not replace APIs or MCP. Traditional APIs remain useful for predictable software interactions, while MCP helps agents connect to tools and data. A2A adds another layer: agent-to-agent collaboration.
For technology leaders, the question will increasingly move from “Which AI agent should we build?” to “How will our agents securely collaborate with other agents, systems, and organizations?”
Building an AI system where agents, tools, cloud platforms, and connected devices need to work together?
Infolitz helps technology teams design practical architectures across AI, IoT, cloud, and software integration, from early architecture decisions to production implementation.
Talk to Infolitz about your agentic AI architecture.
