Background and challenges
In enterprise AI applications, intelligences developed by different vendors usually use closed architectures, resulting in difficult cross-system collaboration and isolated data, greatly limiting the efficiency of automated processes. This is exactly the core problem that the Google A2A protocol is trying to solve.
prescription
- Standardized communication protocols: Implement capability discovery through A2A's AgentCard (JSON format), where intelligences can actively declare their own functionality, interface address, and authentication methods
- Mandate coordination mechanismsBuilt-in task creation/assignment/status tracking functionality for long duration tasks (e.g., reimbursement approval process) and real-time status updates via SSE.
- Mixed mode supportCompatible with text, forms, audio and video and other forms of interaction, to adapt to the needs of different business scenarios
Implementation steps
- Download the official SDK (Python/JavaScript) and deploy the base server
- Extended business logic based on A2AServer class (sample code provides templates for echo intelligences)
- Use A2AClient to connect to other intelligences, call send_message or submit_task to initiate collaboration
- pass (a bill or inspection etc)Protocol specification documentEnsure cross-platform compatibility
caveat
It is recommended to test the reimbursement approval example in the samples directory with a virtual environment first, and then gradually migrate to the production environment. In case of performance issues, streaming can be enabled to optimize real-time performance.
This answer comes from the articleA2A: Google releases open protocol for communication between AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of different intelligences not being able to interoperate and collaborate in an enterprise AI environment?
