A2A Protocol: Breaking the Agent "Island" and Ushering in a New Era of Intelligent Collaboration

Currently, AI systems deployed by enterprises face three major pain points:

1. Data silos
   - HR, finance, and supply chain AI are independent and cannot call data across systems
2. Barriers to collaboration
   - It is difficult for AI developed by different manufacturers (such as Salesforce customer service robot vs SAP ERP system) to communicate directly
3. Efficiency bottleneck
   - Complex tasks require manual connection of multiple AIs, which is time-consuming and error-prone

Case : When a company was recruiting, the HR system AI was unable to directly verify the qualifications of candidates, and manual switching between the background check platform and the payroll system was required, resulting in a recruitment cycle of up to 45 days.

A2A was created to solve these pain points and allow agents from different frameworks and vendors to collaborate seamlessly like a human team.

A2A's Core Philosophy

The agreement framework developed by Google and its 50+ partners revolves around five core concepts:

Technical highlights :

• Agent Card
  - Each AI declares its capabilities (such as "salary calculation", "legal compliance review") through a JSON format "business card".

// Agent Card example { "agent_id": "inventory-manager", "capabilities": ["stock_prediction", "reorder_optimization"], "response_formats": ["json", "text"], "authentication_required": true}

• Task lifecycle management
  -  A2A defines the complete lifecycle of task objects, supporting full-process tracking from task creation, execution, status update to completion. For long-running tasks, the agent can continuously provide real-time feedback and status updates.
• Real-time negotiation mechanism
  - AIs can autonomously negotiate data formats (e.g., requiring the other party to provide 4K video instead of text reports ).

The A2A protocol defines three types of roles:

• User (End User)
  -The  end user (human or service) who uses the agent system to complete a task.
  Client
  -An  entity (service, agent, application) that performs actions on behalf of a user from a remote agent.
  Remote Agent
  - Task executors, who attempt to provide correct information or take correct actions (such as background check platform AI).

1. Ability Discovery - "Social Business Card"

Technical implementation : Each agent declares itself through an "Ability Passport" (Agent Card) in JSON format, which contains key information such as skill labels (such as "financial auditing" and "image recognition"), service interface addresses, and authentication keys.

Scenario Reshaping : ▸  Analogy to e-commerce platforms : Just like Taobao merchants display "main categories", "delivery time" and "customer service response time" on the store homepage, when purchasing clients browse the "capability passport" of supplier agents, they can quickly identify who can provide the fastest logistics and the most stringent quality inspection. ▸  Case upgrade : The product selection AI of a cross-border e-commerce company scans the passports of suppliers across the entire network and locks in partners with "multilingual customer service" and "EU CE certification" capabilities in 3 seconds, eliminating the traditional manual price comparison process.

2. Task Management - "Work Order System"

Technology Upgrade :

• Dynamic Priority
  -Added  "urgency tags" (such as S/A/B levels) to task objects to trigger different response strategies.
• Cross-chain evidence storage
  -Critical  task records are synchronized to the blockchain to prevent collaboration disputes (such as logistics AI falsely reporting receipt time ).

Scenario Reshaping : ▸  Analogy to takeaway dispatch : Just like the Meituan system breaks down orders into "order taking → cooking → delivery" subtasks, the A2A agent breaks down cross-border procurement into "price comparison → contract → customs clearance → logistics" nodes, and each link automatically triggers the next process. ▸  Case upgrade : When a car company was short of chips, the procurement AI created an S-level task, forcing the supplier AI to report inventory fluctuations every 15 minutes, and automatically switched to the backup channel if there was no response after the timeout.

3. Collaboration - "Cross-domain group chat"

Technology Enhancements :

• Context inheritance
  - The dialogue is automatically associated with historical tasks (such as "Refer to the handling plan of similar cases in Q4 2023").
• Semantic Correction
  - Built-in dictionary of industry terms (medical AI says "contraindications", legal AI automatically converts to "liability exclusion clause").

Scenario Reshaping : ▸Analogous  to emergency consultation : Just like the multidisciplinary consultation group of a tertiary hospital, when the critical care AI initiates a request for emergency treatment, the pharmacology AI, image recognition AI, and insurance accounting AI are simultaneously connected to cross-verify the treatment plan in real time. ▸Case  upgrade : When a bank's risk control AI discovers an abnormal transaction, it automatically forms a temporary group including the public security anti-fraud AI and the central bank's credit investigation AI, and freezes 18 related accounts in 3 minutes.

4. User experience negotiation - "scenario adaptation"

Technological innovation :

• Device fingerprinting
  -Automatically  detect user device model, network environment (5G/Wi-Fi), and screen size.
• Cross-modal conversion
  -Supports  17 types of adaptive outputs such as voice report conversion to text and image summaries, video stream frame extraction and key images, etc.

Scene Reshaping : ▸Analogous  to smart home : Just like the Xiaomi speaker automatically switches to night broadcast mode in the bedroom and starts the theater sound field in the living room, the A2A agent generates "voice briefing + key marks" for mobile users and outputs "interactive data dashboard" for decision-makers. ▸Case  upgrade : A geological disaster warning AI sends "broadcast audio + text messages" to the mountainous area and pushes "3D geological model + drone aerial photography live stream" to the emergency command center.

IV. Implementation scenario: the leap from concept to productivity

Scenario 1: Smart Recruitment

• There is no manual intervention in the whole process, and each link comes from different manufacturers
  Publish requirements →  Recruitment platform AI screens resumes →  Background check AI verifies information →  Salary AI generates plans →  Calendar AI arranges interviews

Scenario 2: Supply Chain Crisis Response

• Real-time data integration across systems improves response speed
• Typhoon warning triggers logistics AI to start Plan B →  Supplier AI negotiates alternatives →  Production AI adjusts schedule →  Customer service AI notifies customers of delay

Google positions A2A as a complement to Anthropic’s Model Context Protocol (MCP). If MCP is a socket wrench (for tools), then A2A is a conversation between mechanics (for collaboration). The two protocols complement each other perfectly in terms of functionality:

• •  MCP : Focuses on connecting a single AI model to external tools and data sources (model to data/tools)
• •  A2A : Focuses on communication and collaboration between multiple AI agents (agent to agent)