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Starts 8 June 2025 12:05
Ends 8 June 2025
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Agent2Agent and MCP Protocol in Multi-Agent AI
Explore Google's Agent2Agent Protocol and its compatibility with MCP protocol for tool use by LLMs, featuring cross-ecosystem compatibility with LangGraph and crew.ai, plus Python code examples and JSON schema details.
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Overview
Explore Google's Agent2Agent Protocol and its compatibility with MCP protocol for tool use by LLMs, featuring cross-ecosystem compatibility with LangGraph and crew.ai, plus Python code examples and JSON schema details.
Syllabus
- Introduction to Multi-Agent AI
- Google's Agent2Agent Protocol
- MCP Protocol Overview
- Compatibility and Cross-Ecosystem Integration
- Python Code Examples and Implementations
- JSON Schema Details
- Case Studies and Real-World Applications
- Future Trends in Multi-Agent Protocols
Overview of Multi-Agent Systems
Importance and Applications in AI
Definition and Role in Multi-Agent Systems
Architecture and Key Components
Communication Patterns and Protocol Features
Integration with Large Language Models (LLMs)
Key Features and Principles
Comparison with Agent2Agent Protocol
Application in Multi-Agent Collaboration
Overview of LangGraph
Overview of crew.ai
Integration Strategies for Agent2Agent with MCP Protocol
Ensuring Compatibility across Systems
Basic Examples of Agent2Agent Implementation
Using MCP Protocol with Python
Cross-Communication between Agents using Python Scripts
Understanding JSON Schemas in Protocols
Structuring Data with JSON for Agent Communication
Practical Examples and Templates
Successful Implementations of Agent2Agent and MCP
Lessons Learned from Multi-Agent AI Deployments
Emerging Technologies and Innovations
Potential Enhancements in Protocol Designs
Subjects
Computer Science
