Why & How A2A & MCP | Making An Agent2Agent Server
AI Summary
Overview of A2A Protocol
- The A2A (Agent-to-Agent) protocol is discussed in contrast to the MCP (Model Context Protocol).
- The speaker clarifies their understanding and the differences between A2A and MCP, particularly in terms of functionality and integration.
- An assistant is built to demonstrate the A2A protocol’s capabilities.
Key Features of A2A
- The A2A protocol allows agents to handle tasks independently, maintaining context and memory efficiently.
- It supports deep research capabilities, returning summarized reports without cluttering the conversation context.
- Agents use their own LLM (Large Language Model) to manage requests and responses, which is highlighted as a significant advantage.
Implementation Insights
- A chat application is developed that interfaces with A2A and MCP, translating tasks to LLM tools.
- The structure allows agents to report progress and send artifacts (like markdown files) back to users.
- The necessity for both A2A and MCP functionalities is discussed, emphasizing their compatibility and interoperability.
Practical Application
- The speaker demonstrates the construction of the A2A client and server, explaining the use of async generator functions for task management and progress tracking.
- The implementation of a deep research agent is showcased, extracting insights from extensive web resources and providing concise reports.
- The nuances of building an effective communication channel between chat applications and A2A agents are examined, including the handling of user input needs and task interruptions.
Conclusion
- The speaker expresses a desire to contribute to the community by sharing insights and tools for the A2A protocol, encouraging viewers to engage and explore enhancements further.
- An invitation for feedback and suggestions for future discussions is extended, closing with gratitude for viewer engagement.