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A2A Protocol Simply Explained: Here are 3 key differences to MCP!
- Google's Agent-to-Agent (A2A) protocol aims to enable AI assistants to collaboratively work together, unlike working in isolation.
- A2A turns AI assistants into team players, allowing seamless passing of information and collaboration on tasks.
- Comparing A2A to Anthropic's Model Context Protocol (MCP) reveals different approaches to AI system collaboration.
- MCP enables AI assistants to access specialized tools, while A2A connects specialized AI assistants to each other.
- A2A allows for delegation of tasks among specialized AI assistants, facilitating complex problem-solving.
- MCP provides a standard way for any AI assistant to connect to any tool, enhancing their capabilities.
- A2A orchestrates tasks among AI assistants, streamlining processes for users without the need for manual intervention.
- The collaboration between MCP and A2A creates an ecosystem where AI assistants with different capabilities can work together effectively.
- A2A communication is more human-like, while MCP focuses on structured schema for precise operation.
- A2A manages tasks through multi-stage processes, while MCP treats operations as individual function calls.
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