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AI Multi-Agent MAS MCP A2A Python LangGraph Agent Collaboration

Multi-Agent Systems: MCP, A2A and Orchestrated Collaboration

Three-day intensive training on developing multi-agent systems with MCP and A2A protocol - from theory to practical implementation.

3 Days
Advanced
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Multi-Agent Systems: MCP, A2A and Orchestrated Collaboration

Course Overview

This intensive 3-day advanced course introduces you to the world of Multi-Agent Systems (MAS). You’ll learn how multiple specialized AI agents effectively collaborate to solve complex tasks. The course covers both the Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, showing how these technologies work together. You’ll develop practical multi-agent applications with various communication patterns and orchestration strategies.

Prerequisites: Basic knowledge of AI agents, Python, and ideally experience with LLMs (e.g., from our course “AI Agents: From Fundamentals to Practice”).

What You Will Learn

Day 1: Fundamentals and Theory of Multi-Agent Systems

  • Introduction to Multi-Agent Systems (MAS)

    • What are multi-agent systems?
    • History: From distributed systems to AI agents
    • Why multi-agent instead of single-agent?
    • Complexity management through specialization
    • Scalability and robustness
    • Real-world use cases: Research, software development, data analysis

  • MAS Architectures Overview

    • Centralized vs. decentralized architectures
    • Hierarchical systems (Supervisor/Manager pattern)
    • Peer-to-peer networks (equal agents)
    • Hybrid architectures
    • Pipeline vs. graph-based workflows
    • Trade-offs and selection criteria

  • Hands-on Exercise: MAS Analysis

    • Analysis of 3 real multi-agent scenarios
    • Identification of agent roles and interactions
    • Understanding architecture decisions
    • Discussion: Which pattern for what?

  • Communication Between Agents

    • Message-based communication
    • Shared memory vs. message passing
    • Synchronous vs. asynchronous communication
    • Event-driven architectures
    • Protocols and standards

  • Hands-on Exercise: Architecture Design

    • Use case: E-commerce order processing
    • Design a multi-agent system on paper
    • Define agent roles and responsibilities
    • Establish communication paths
    • Peer review and discussion

  • Coordination and Collaboration

    • Task delegation and workload balancing
    • Consensus mechanisms
    • Conflict resolution
    • State management in distributed systems
    • Fault tolerance and recovery

  • Introduction to A2A Protocol

    • What is A2A? Google’s vision for agent communication
    • Motivation: Standardizing agent interaction
    • A2A vs. other protocols
    • Core concepts and components
    • Message format and structure

  • First A2A Implementation

    • Development environment setup
    • Simple A2A agent in Python
    • Implementing agent registration
    • Capability definition
    • Hands-on Exercise: “Hello World” A2A system

  • A2A Protocol Specification

    • Agent discovery and registration
    • Capability advertisement
    • Request/response patterns
    • Streaming and long conversations
    • Error handling and status codes
    • Metadata and context passing

Day 2: A2A Implementation and MCP Integration

  • A2A Message Exchange

    • Message structure in detail
    • Implementing request handling
    • Response generation
    • Context and conversation state
    • Hands-on Exercise: Two-agent dialogue

  • Complex A2A Scenarios

    • Multi-turn conversations
    • Agent chaining: Output becomes input
    • Parallel agent calls
    • Error handling and retries
    • Hands-on Exercise: Weather + Planner agents via A2A

  • MCP Review and Deep Dive

    • MCP architecture: Server, client, transport
    • Resources, tools, prompts
    • MCP vs. A2A: Differences and commonalities
    • When to use which protocol?

  • MCP and A2A: Working Together

    • A2A for agent-to-agent, MCP for tool integration
    • Architecture patterns: A2A + MCP
    • Agent consumes MCP server
    • A2A agents with MCP tools
    • Best practices for hybrid systems

  • Weather Agent Migration

    • Analysis of existing weather agent
    • Design of weather MCP server
    • MCP server implementation (Weather API)
    • Agent migration: From direct API to MCP
    • Hands-on Exercise: Develop weather MCP server

  • A2A Agent with MCP Integration

    • Equipping agent with MCP client
    • Tool discovery via MCP
    • Tool execution via MCP
    • Response handling and formatting
    • Hands-on Exercise: Weather agent via A2A with MCP backend

Day 3: LangGraph Orchestration and Integration

  • LangGraph for Multi-Agent Orchestration

    • Why LangGraph for MAS?
    • Graph concept: Nodes, edges, state
    • Conditional routing and branching
    • Loops and iterative processes
    • StateGraph vs. MessageGraph

  • Network Architecture with LangGraph

    • Peer-to-peer agents as nodes
    • Decentralized decision-making
    • Agent selection logic
    • Message passing between nodes
    • Hands-on Exercise: 3-agent network system

  • Supervisor Architecture with LangGraph

    • Supervisor node: The orchestrating agent
    • Worker nodes: Specialized agents
    • Dynamic task routing
    • Result aggregation
    • Hands-on Exercise: Research team (Supervisor + 4 workers)

  • A2A + MCP + LangGraph: The Complete System

    • Integration of all protocols
    • Multi-agent system with:
      • LangGraph for orchestration
      • A2A for agent communication
      • MCP for tool integration
    • Hands-on Exercise: Content creation pipeline
    • Step-by-step integration
    • Architecture design and decisions
    • Best practices and patterns

  • Debugging and Observability

    • LangSmith for multi-agent debugging
    • Tracing across multiple agents
    • Message flow visualization
    • Performance monitoring
    • Bottleneck identification
    • Error tracking in distributed systems

  • Production-Ready MAS

    • Scaling multi-agent systems
    • Load balancing and resource management
    • Security considerations
    • Monitoring and alerting
    • Testing strategies for MAS
    • CI/CD for agent systems
    • Deployment strategies
    • Maintenance and updates

Prerequisites

  • Required: Basic knowledge of AI agents and Python
  • Recommended: Completion of “AI Agents: From Fundamentals to Practice” or equivalent
  • Helpful: Experience with APIs, async Python, LLMs
  • System: Laptop with Python 3.10+, Docker (optional), 16GB RAM recommended
  • Preparation: Ollama installed, LangGraph account created

Course Format

  • Duration: 3 full days (24 hours)
  • Format: Advanced hands-on workshop with live coding
  • Class Size: Maximum 10 participants for intensive support
  • Materials: Complete source code, architectures, best practices
  • Support: 4 weeks post-training support via Discord

Technology Stack

In the course, we work with:

  • Python 3.10+: Main programming language
  • A2A Protocol: Google Agent-to-Agent Communication
  • MCP SDK: Model Context Protocol
  • LangGraph: Multi-agent orchestration
  • LangSmith: Debugging and observability
  • Ollama: Local LLM inference
  • FastAPI: For agent servers (optional)
  • Docker: Containerization (optional)

Hands-on Projects

During the course, you will develop:

Day 1: Theory and A2A Fundamentals

  1. Basic A2A Communication
    • Two agents with A2A protocol
    • Request/response pattern
    • Message format validation

Day 2: A2A Practice and MCP Integration

  1. Weather + Planner System

    • Weather agent: Retrieve weather data
    • Planner agent: Suggest activities
    • A2A-based communication
    • Multi-turn conversation

  2. Agent Discovery System

    • Agent registry
    • Capability advertisement
    • Dynamic agent selection

  3. Weather MCP Server

    • MCP server for weather APIs
    • Multiple tools (current, forecast, historical)
    • Integration into existing agent

Day 3: LangGraph and Complete Integration

  1. Network Multi-Agent System

    • 3 peer agents with LangGraph
    • A2A communication
    • MCP tool integration

  2. Supervisor Multi-Agent System

    • Supervisor + 4 workers (Researcher, Analyst, Writer, Reviewer)
    • LangGraph orchestration
    • Hierarchical coordination

  3. Production-Ready System (Main Afternoon Project)

    • Complete integration of all protocols:
      • A2A for agent communication
      • MCP for tool integration
      • LangGraph for orchestration
    • Content creation pipeline
    • Logging and monitoring
    • Error handling and resilience
    • Testing suite
    • Deployment-ready implementation

After the Course You Can

✅ Design and implement multi-agent systems
✅ Use A2A protocol for agent communication
✅ Develop MCP servers and integrate into MAS
✅ Combine A2A and MCP for robust systems
✅ Use LangGraph for complex multi-agent workflows
✅ Implement network and supervisor architectures
✅ Debug and monitor multi-agent systems
✅ Develop and deploy production-ready MAS
✅ Solve scaling and performance problems
✅ Apply coordination strategies for distributed agents

Who Should Attend

This advanced course is for:

  • AI Engineers with agent experience who want to enter MAS
  • Software Architects designing multi-agent architectures
  • ML Engineers building distributed AI systems
  • Tech Leads leading complex agent projects
  • Developers with prior knowledge in AI agents (see prerequisites)

Not suitable for: Beginners without agent knowledge (see beginner course)

Course Materials

You will receive:

  • Complete source code of all projects
  • A2A protocol reference and cheat sheet
  • MCP integration guide
  • LangGraph multi-agent patterns
  • Architecture diagrams for all systems
  • Best practices and anti-patterns
  • Production deployment checklist
  • Access to code repository with updates