How the Agent2Agent Protocol (A2A) Actually Works: A Technical Breakdown

The Agent2Agent Protocol now has support from more than 50 major technology partners, including Atlassian, Salesforce, and Deloitte. This represents a transformation in AI agent communication methods. AI technology advances faster each day, yet enterprises still face their biggest problem - making different AI agents work together effectively.

The A2A protocol provides a complete solution for secure, cross-platform communication between AI agents through HTTP, SSE, and JSON-RPC standards. The protocol also handles text, audio, and video streaming interactions while meeting enterprise security requirements. Users can execute complex workflows across multiple systems, from basic tasks to longer operations such as multi-day job candidate sourcing.

This technical breakdown shows how the Agent2Agent Protocol operates by exploring its architecture, capability discovery mechanisms, task management lifecycle, and security implementations. You'll learn about the technical foundations that enable agent-to-agent collaboration across different platforms and vendors.

Core Architecture of the A2A Protocol

The Agent2Agent (A2A) protocol provides a strong technical foundation that lets AI agents communicate across different platforms and vendors. A2A works through well-laid-out communication patterns, standard message formats, and reliable data transfer methods that help agents interact securely and quickly.

Client-Remote Agent Communication Model

A2A's basic architecture centers on two agent types working together: client agents and remote agents. Client agents create and send tasks to the right remote agents. Remote agents process these tasks and return accurate information or complete specific actions. This clear division of duties creates a system where specialized agents can excel at what they do best.

Agents work on their own and don't share memory or tools by default. They share information through structured messages. This "agentic-first" approach lets agents keep their unique abilities while working together smoothly across organizations.

The communication follows these steps:

1. Client agent spots a task needing outside expertise
2. Client agent finds the right remote agent through capability discovery
3. Client agent creates and sends a task request
4. Remote agent processes the request and creates artifacts
5. Client agent gets and uses the response

This setup supports quick information requests and complex projects that might take weeks to complete.

JSON-RPC Implementation for Message Exchange

A2A uses JSON-RPC 2.0 to handle message exchange. JSON-RPC offers a simple, language-independent way to make remote procedure calls using JSON data format. This choice helps A2A stay simple while handling complex agent interactions.

Messages contain structured "parts" – complete content pieces with specific types. Each part can include different formats like text, images, or audio, which allows for flexible interactions. The JSON-RPC layer manages how agents call methods, pass parameters, and get results. This creates a standard way for agents to talk to each other.

This message structure makes integration easier by standardizing how agents ask for services. Developers can create agent interactions without worrying about how each agent works internally.

HTTP and SSE Foundation for Data Transfer

The protocol uses HTTP as its main transport layer, which builds on standard web communication. This makes A2A easy to integrate with existing systems and development tools.

A2A includes Server-Sent Events (SSE) for up-to-the-minute data analysis during long tasks. SSE lets remote agents stream updates to clients as work progresses. This helps with complex tasks like hiring processes or supply chain planning that take time to finish.

HTTP/SSE brings several advantages:

• Real-time task status updates through tasks/sendSubscribe endpoints
• Push notifications via webhook URLs with proper authentication
• Efficient streaming to deliver text, audio, and video content

This setup lets agents maintain lasting connections to work together without constant reconnection.

Protocol Versioning and Compatibility

A2A includes a versioning system to manage different implementations. Compatibility levels show which features clients can access based on their protocol version.

This versioning lets older agent versions work alongside newer ones. Teams can protect their investment in agent development and add new features gradually.

These architectural pieces create a flexible system for agents to work together. A2A builds on proven standards and focuses on agent-centered design. This creates a strong foundation for complex agent systems that can grow without breaking existing setups.

Capability Discovery Mechanism

Capability discovery is a fundamental pillar of the agent2agent protocol (A2A). It makes intelligent agent interactions possible beyond ecosystem boundaries. Agents can advertise their skills and find capabilities of other agents in the network. This creates a foundation that enables meaningful collaboration.

Agent Card Structure and JSON Format

The "Agent Card" is at the heart of A2A's discovery mechanism. This standardized metadata file sits at /.well-known/agent.json and declares an agent's functional capabilities. The Agent Card acts as the agent's digital identity within the A2A ecosystem. It contains vital information that client agents need to build meaningful connections.

The JSON-formatted Agent Card has several key components:

• Capabilities and skills the agent can perform
• Endpoint URL to receive A2A requests
• Authentication requirements to secure access
• Protocol version compatibility information

Client agents need to perform tasks that require external expertise. They first get the Agent Card from a remote agent's well-known URL. This discovery process works like web browsers finding robots.txt files. It creates a predictable location to store capability information across the A2A network.

The Agent Card format must follow strict JSON formatting rules. This makes it readable by both humans and machines. Different implementations might add extra fields based on specific needs. The core structure stays consistent throughout the ecosystem. This ensures different vendor implementations work together smoothly.

Dynamic Capability Registration Process

A2A's approach is different from static API descriptions. It offers a dynamic capability registration process where agents can advertise new functions as they become ready. This is substantially different from traditional capability exchange systems that need session resets to handle new capabilities.

The dynamic registration process works in stages:

Agents register their capabilities through the Agent Card. This makes them visible to client agents. Agents can update their Card with new capabilities or changes without disrupting ongoing communications. The system gives agent ecosystems the flexibility to grow without needing to rebuild all connections.

Remote agents control their capability declarations completely. They can set up detailed access control based on client identity, authentication status, or other factors. To cite an instance, an agent might show different capabilities to authenticated and anonymous users. This ensures appropriate access levels based on security needs.

Capability Query Parameters and Filtering

Client agents often need to sort through available functions to find relevant ones. A2A handles this with a detailed query parameter system that allows precise capability filtering.

The protocol supports several filtering mechanisms:

• Simple equality filters that match exact capability attributes
• Range-based queries for numerical capability properties
• Regular expression patterns for flexible string matching
• Multi-field filtering with logical operators (AND/OR)

Capability queries follow common patterns from database query languages. This makes them natural for developers who know these concepts. Clients can filter capabilities by specific content types or authorization requirements.

A2A sets limits to prevent system abuse. URL query strings cannot exceed 2000 characters. Filter expressions typically max out at 10 combined conditions. These limits help maintain good performance without affecting most real-world uses.

Clients can use the property parameter with comparison operators to refine complex capability searches. They can combine greater-than and less-than operators with boolean expressions. This powerful filtering helps clients find exact capabilities they need in large agent ecosystems.

The A2A protocol builds a reliable discovery framework through these three connected mechanisms – structured Agent Cards, dynamic capability registration, and flexible query parameters. This framework works well across many agent implementations. This standardized approach to capability discovery represents major progress compared to older, less organized agent communication methods.

Task Management Lifecycle

Task management serves as the foundation of the agent2agent protocol. This system makes shared communication possible and helps complete specific user requests. The A2A ecosystem centers around "tasks" that move through specific lifecycle states, produce outputs, and manage potential errors.

Task Object Structure and Properties

A standardized task object structure is the cornerstone of A2A task management. Each task has several key properties that aid tracking and execution between agent boundaries. The task object has:

• id: A unique string identifier for the specific task
• sessionId: An optional string that groups related tasks together
• status: The current TaskStatus object containing state and message information
• artifacts: Array of outputs generated by the task (may be null initially)
• history: Optional array of messages exchanged during task execution
• metadata: Additional task-related information in object format

This well-laid-out approach delivers consistency in agent implementations whatever their underlying frameworks. The task object acts as a contract between client and remote agents and keeps a clear record of progress and outcomes during execution.

State Transitions in Long-Running Tasks

A2A protocol tasks follow a defined progression through states that support immediate and extended operations. The protocol defines these task states:

1. submitted: Task received but not yet started
2. working: Active processing underway
3. input-required: Agent needs additional information from client
4. completed: Task successfully finished
5. canceled: Task terminated before completion
6. failed: Task encountered unrecoverable error

State transitions include a timestamp and might contain context messages. This approach helps agents stay synchronized on task progress, which matters most for operations that take hours or days to complete.

A2A provides immediate feedback, notifications, and state updates to users. Users get a transparent view of task execution even when it crosses organizational boundaries.

Artifact Generation and Handling

A2A protocol formalizes task outputs as "artifacts". Each artifact delivers structured content back to the requesting client and contains:

• name: Optional identifier for the artifact
• description: Human-readable explanation of the content
• parts: Array of content segments with specified formats
• index: Ordering value for proper sequencing
• append: Boolean flag indicating if content should append to existing artifact
• lastChunk: Boolean marker for the final segment in streaming scenarios

Artifacts can include metadata that adds context about the generated content. This structured output handling approach supports everything from simple text responses to complex multimodal content like images, audio, or video.

The CrewAI sample implementation shows how image generation works as artifacts. The server-client flow maintains standard A2A protocol patterns despite different underlying implementations.

Error Handling and Recovery Patterns

Tasks may face issues during execution. The A2A protocol uses resilient error handling patterns to manage these situations. The protocol uses a hierarchical approach to recovery when errors occur:

• Recovery agents handle specific errors like "part loaded incorrectly" at the workstation level
• Specialized recovery agents arbitrate complex errors through mediator components
• The protocol can pause execution and get input for cases needing human intervention

The protocol separates production and recovery agents. This allows specialization in error handling and keeps the system's structure intact. Even failed tasks provide appropriate feedback to clients.

Agent clusters can quickly adapt to disruptions while maintaining the protocol's hierarchical structure. This balances flexibility with stability throughout task management.

Implementing Agent Collaboration

Agent-to-agent cooperation represents the A2A protocol's architectural principles in action. The protocol excels through its ability to enable dynamic, multimodal communication between different agents. Agents don't need to share memory, resources, or tools - a vital capability for enterprise environments.

Message Exchange Patterns

The A2A protocol's communication follows structured message patterns that ensure reliability and clarity. Agents exchange messages containing "parts" - complete content pieces with specific content types that make multimodal cooperation possible. This system lets agents share and process text, audio, and video in unified processes.

Simple request-response patterns with acknowledgments form the message exchange:

1. Client agent sends a formatted message to remote agent
2. Remote agent acknowledges receipt
3. Remote agent processes request and generates response
4. Client agent acknowledges the response

Each exchange in A2A's messaging structure contains context, replies, artifacts, or user instructions. This standardization helps agents exchange information effectively, whatever their vendor origin. The protocol supports agentic-first design where agents work independently and communicate explicitly rather than sharing internal states.

Context Sharing Between Agents

A2A stands apart from traditional AI systems by supporting opaque agents that don't reveal their internal reasoning or memory. This design serves as a vital component for enterprise use cases where security, modularity, or vendor abstraction matters most.

Agents share context through well-laid-out "Tasks" instead of syncing internal states. These tasks contain:

• Inputs and instructions
• Results (formatted as "Artifacts")
• Live status updates
• Execution history

This approach solves a key challenge in agent ecosystems: keeping agents separate while enabling teamwork. To cite an instance, see a hiring workflow where specialized agents handle different parts of the process (sourcing, vetting, scheduling) without exposing proprietary algorithms or data.

Task-oriented context sharing defines A2A's approach. Agents cooperate effectively while maintaining data boundaries - a necessity for enterprises that manage sensitive information across systems.

Handling Asynchronous Responses

A2A protocol manages brief interactions and extended processes through resilient asynchronous response handling. This feature supports long-running tasks that might take days, weeks, or even months to finish - like supply chain planning or multi-stage hiring processes.

Server-Sent Events (SSE) power A2A's asynchronous communication for streaming updates from remote agents. This enables immediate feedback, notifications, and state updates throughout task execution. Agents that need extra processing time can:

• Send immediate acknowledgments
• Process requests at their pace
• Give continual status updates
• Deliver results when ready

This method mirrors HTTP's asynchronous request-reply pattern where servers respond with an acknowledgment (HTTP 202) and offer a status endpoint for polling. A2A implementations let agents "subscribe" to task updates and receive pushes as work progresses instead of constant polling.

A2A supports enterprise workflows across organizational boundaries, cloud environments, and vendor ecosystems through asynchronous response handling. This happens without compromising performance or user experience.

User Experience Negotiation

User interactions in the agent2agent protocol demand a sophisticated approach to experience delivery. A2A lets agents negotiate and adapt their communication with users live. This creates uninterrupted experiences in a variety of platforms and interfaces, unlike traditional APIs.

Content Type Specification

A2A uses a well-laid-out approach to content type definition through "parts" within each message. Each part represents a complete piece of content with explicitly specified content types. Client and remote agents can precisely negotiate the formats they need to communicate effectively. The protocol helps agents define content types for various elements:

• Text responses with formatting information
• Image data with resolution specifications
• Audio streams with quality parameters
• Interactive elements with behavior definitions

Agents maintain consistent content presentation across different environments by defining specifications at the message level. This approach is different from traditional content negotiation. It works at a more detailed level and allows individual message components to carry their own format definitions.

UI Capability Detection

A2A goes beyond simple content typing with smart UI capability detection. The protocol supports negotiations about the user's interface capabilities—including iframes, video players, web forms, and other interactive elements. The detection happens live and allows:

1. Clients to communicate available display capabilities
2. Remote agents to adapt responses accordingly
3. Sessions to evolve as new UI requirements emerge

An agent can check whether a user's interface supports complex visualizations before delivering them. The agent can switch to simpler presentations without disrupting the interaction flow when advanced UI capabilities aren't available.

A2A stands out with its support for live UX negotiation within a task. Agents can add audio or video capabilities during conversations as needs change. This creates adaptable interfaces that respond to evolving requirements.

Multimodal Content Handling (Text, Audio, Video)

Modern AI interactions go far beyond text exchanges. The agent2agent protocol supports multimodal collaboration. AI agents can share and process text, audio, and video within unified workflows. This approach creates several advantages.

A2A's design helps agents handle various data types at once through structured task updates. A technical support scenario might see agents exchange text troubleshooting steps while sharing video demonstrations and audio explanations—all within the same protocol structure.

Multimodal capabilities serve as a core requirement that drives the next generation of interconnected agent systems. A2A's structured approach to handling diverse media types becomes more valuable as interactions grow complex. This helps maintain coherence across agent collaborations.

The multimodal framework proves especially valuable in enterprise settings. Information often exists in different formats and systems. A2A delivers consistent experiences through standardized handling of text, audio, and video, regardless of data complexity.

Security Implementation in A2A

Security forms the foundation of the agent2agent protocol rather than being just an add-on feature. A2A was built from scratch with enterprise-grade security measures that protect sensitive data and make shared collaboration possible between AI systems.

Authentication Mechanisms

A2A offers reliable authentication options matching OpenAPI's authentication schemes. The protocol supports JSON Web Tokens (JWT) as an alternative to traditional user authentication and allows multiple concurrent API sessions. RSA key pairs provide secure access for service account authentication. This requires public key registration and private key signature generation. The approach lets systems interact programmatically while maintaining strong security boundaries between services.

Authorization Models

A2A implements complete authorization through role-based access control beyond authentication. Only agents with correct credentials and permissions can participate in critical workflows or access protected data streams. Service accounts can get specific roles that limit access to what each agent needs. Authorization mechanisms adapt dynamically based on context, which creates flexible security that changes with requirements.

Data Encryption Requirements

A2A requires encrypted data exchanges throughout the communication lifecycle alongside access controls. All sensitive information between agents stays encrypted to prevent unauthorized access. AI systems often handle confidential data - from personal information in hiring processes to financial records. This protection layer becomes crucial for enterprise adoption.

Rate Limiting and Abuse Prevention

A2A uses various rate limiting strategies to prevent resource abuse and protect against attacks:

• Token bucket algorithm: Adds tokens at a fixed rate, removing one per request
• Leaky bucket algorithm: Requests drain from a bucket at a constant rate
• Rolling window algorithm: Counts requests within a sliding time window

These mechanisms guard against DDoS attacks, brute force attempts, and API abuse. They use IP-based, server-based, or geography-based approaches to create multiple defensive layers for the agent ecosystem.

Conclusion

The Agent2Agent Protocol offers a complete solution that helps enterprise AI agents communicate effectively. It uses a reliable system built on HTTP, SSE, and JSON-RPC that allows AI systems to interact securely across different organizations.

Our technical analysis shows these major strengths of the protocol:

• Message patterns that support many types of communication
• Smart ways to learn about agent capabilities that make interactions flexible
• Advanced task handling that works for both quick requests and longer projects
• Security features that keep sensitive data safe at enterprise level

The protocol handles real-life challenges smartly. AI agents don't need to share their internal workings. They can work on their own while staying connected through clear communication channels. This approach works great for companies that need to protect sensitive information across different systems and vendors.

Major technology partners have tested and confirmed the protocol's effectiveness. The A2A's foundations will support more complex agent interactions as AI systems advance. Companies that use this protocol get a future-ready system to handle AI agent communications throughout their tech stack.