Team Based Artchitectures

Multi-Agent Architectures

Multi-agent systems distribute complex tasks across specialized agents, each with distinct roles and capabilities. This architecture provides several advantages:

1. Specialization

   • Agents can focus on specific domains
   • Reduced prompt complexity per agent
   • Clear separation of concerns

2. Scalability

   • Parallel processing capabilities
   • Distributed decision making
   • Independent scaling of components

3. Robustness

   • Graceful degradation
   • Fault isolation
   • Easier maintenance

Team-Based Agent Architecture

A team-based architecture typically consists of three main components:

1. Supervisor Agent

   • Coordinates team activities
   • Makes routing decisions
   • Ensures task completion

2. Specialist Agents

   • Handle domain-specific tasks
   • Maintain focused expertise
   • Provide detailed analysis

3. State Management System

   • Maintains conversation context
   • Tracks team progress
   • Manages shared resources

Implementing a Team-Based System

1. Supervisor Agent Implementation

def create_supervisor_agent(llm: ChatOpenAI):
    """
    Create a supervisor agent that coordinates team activities.
    
    Key responsibilities:
    - Task decomposition
    - Agent selection
    - Progress monitoring
    - Final response synthesis
    """
    supervisor_prompt = """
    Core responsibilities:
    1. Analyze incoming queries
    2. Determine required information
    3. Select appropriate specialist
    4. Monitor progress
    5. Decide when to conclude
    """
    
    return create_team_supervisor(
        llm=llm,
        system_prompt=supervisor_prompt,
        members=["SpecialistA", "SpecialistB"]
    )

2. Specialist Agent Implementation

def create_specialist_agent(
    llm: ChatOpenAI,
    domain: str,
    tools: List[Tool]
):
    """
    Create a specialist agent with domain-specific capabilities.
    
    Key features:
    - Focused expertise
    - Specific tool access
    - Clear output format
    """
    system_prompt = f"""
    You are specialized in {domain}.
    When responding:
    1. Use domain-specific tools
    2. Provide structured output
    3. Request missing information
    """
    
    return create_agent(
        llm=llm,
        tools=tools,
        system_prompt=system_prompt
    )

3. State Management

class TeamState(TypedDict):
    """
    Manage team-wide state and context.
    
    Components:
    - Message history
    - Team composition
    - Current status
    - Required information
    """
    messages: List[BaseMessage]
    team_members: List[str]
    next: str
    information_needed: List[str]
    reasoning: str

Building the Team Graph

The team graph defines how agents interact and how information flows:

def create_team_graph():
    """
    Create a coordinated team of agents with defined interactions.
    
    Structure:
    1. Initialize components
    2. Define connections
    3. Set workflow rules
    4. Establish entry points
    """
    # Initialize agents
    specialist_a = create_specialist_agent(...)
    specialist_b = create_specialist_agent(...)
    supervisor = create_supervisor_agent(...)
    
    # Create graph
    graph = StateGraph(TeamState)
    
    # Add nodes
    graph.add_node("SpecialistA", specialist_a)
    graph.add_node("SpecialistB", specialist_b)
    graph.add_node("supervisor", supervisor)
    
    # Define workflows
    graph.add_edge("SpecialistA", "supervisor")
    graph.add_edge("SpecialistB", "supervisor")
    
    # Add conditional routing
    graph.add_conditional_edges(
        "supervisor",
        lambda x: x["next"],
        {
            "SpecialistA": "SpecialistA",
            "SpecialistB": "SpecialistB",
            "FINISH": END
        }
    )
    
    return graph.compile()