"""
Pattern 35: Federated Agent Pattern

Autonomous organizational units that maintain independence while coordinating through a federation.
Each unit (federation member) retains local autonomy but adheres to federation protocols.
"""

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI


@dataclass
class FederationMember:
    """Autonomous organizational unit in federation."""
    name: str
    domain: str  # Area of expertise/responsibility
    resources: List[str] = field(default_factory=list)
    policies: Dict[str, Any] = field(default_factory=dict)
    shared_resources: List[str] = field(default_factory=list)
    
@dataclass
class FederationProtocol:
    """Shared rules for federation coordination."""
    name: str
    rules: List[str]
    member_signatures: Set[str] = field(default_factory=set)


class FederatedAgentPattern:
    """
    Federated Agent Pattern - Autonomous units with coordinated governance.
    
    Features:
    - Local autonomy for each member
    - Shared protocols and standards
    - Resource sharing agreements
    - Decentralized decision-making
    - Cross-federation collaboration
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.members: Dict[str, FederationMember] = {}
        self.protocols: Dict[str, FederationProtocol] = {}
        self.resource_exchanges: List[Dict[str, Any]] = []
    
    def add_member(self, member: FederationMember):
        """Add a member to the federation."""
        self.members[member.name] = member
        print(f"✓ Added federation member: {member.name} (Domain: {member.domain})")
    
    def create_protocol(self, protocol: FederationProtocol) -> bool:
        """Create a federation-wide protocol."""
        self.protocols[protocol.name] = protocol
        print(f"✓ Created protocol: {protocol.name}")
        return True
    
    def sign_protocol(self, member_name: str, protocol_name: str) -> bool:
        """Member signs agreement to follow protocol."""
        if member_name in self.members and protocol_name in self.protocols:
            self.protocols[protocol_name].member_signatures.add(member_name)
            print(f"✓ {member_name} signed protocol: {protocol_name}")
            return True
        return False
    
    def share_resource(self, from_member: str, to_member: str, resource: str) -> Dict[str, Any]:
        """Share resource between federation members."""
        if from_member in self.members and to_member in self.members:
            exchange = {
                "from": from_member,
                "to": to_member,
                "resource": resource,
                "timestamp": datetime.now()
            }
            self.resource_exchanges.append(exchange)
            self.members[to_member].shared_resources.append(resource)
            return exchange
        return {}
    
    def coordinate_action(self, action: str, participating_members: List[str]) -> Dict[str, Any]:
        """Coordinate action across multiple members."""
        if not all(m in self.members for m in participating_members):
            return {"success": False, "error": "Invalid members"}
        
        # Get member contexts
        contexts = []
        for member_name in participating_members:
            member = self.members[member_name]
            contexts.append(f"{member.name} ({member.domain})")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are coordinating a federated action. "
                      "Provide a coordination plan that respects member autonomy."),
            ("human", f"Action: {action}\n"
                     f"Participating: {', '.join(contexts)}\n\n"
                     f"Coordination plan:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return {
            "success": True,
            "action": action,
            "members": participating_members,
            "plan": response.content
        }
    
    def run(self, scenario: str) -> Dict[str, Any]:
        """Execute federation scenario."""
        return {
            "pattern": "Federated Agent",
            "scenario": scenario,
            "members": len(self.members),
            "protocols": len(self.protocols),
            "resource_exchanges": len(self.resource_exchanges)
        }


# Example usage
if __name__ == "__main__":
    print("=== Federated Agent Pattern Demo ===\n")
    
    federation = FederatedAgentPattern()
    
    # Add federation members
    print("--- Adding Federation Members ---")
    members = [
        FederationMember("RegionNorth", "Healthcare", ["hospital_data", "clinics"]),
        FederationMember("RegionSouth", "Healthcare", ["medical_centers", "research_labs"]),
        FederationMember("RegionEast", "Healthcare", ["trauma_centers", "specialists"]),
    ]
    
    for member in members:
        federation.add_member(member)
    
    # Create federation protocols
    print("\n--- Creating Federation Protocols ---")
    protocol = FederationProtocol(
        "DataPrivacy",
        ["Encrypt all patient data", "No data sharing without consent", "Annual audits required"]
    )
    federation.create_protocol(protocol)
    
    # Members sign protocols
    print("\n--- Protocol Signatures ---")
    for member in members:
        federation.sign_protocol(member.name, "DataPrivacy")
    
    # Resource sharing
    print("\n--- Resource Sharing ---")
    exchange = federation.share_resource("RegionNorth", "RegionSouth", "specialist_database")
    print(f"✓ Resource exchange: {exchange['from']} → {exchange['to']}: {exchange['resource']}")
    
    # Coordinate action
    print("\n--- Federated Coordination ---")
    result = federation.coordinate_action(
        "Implement unified emergency response system",
        ["RegionNorth", "RegionSouth", "RegionEast"]
    )
    print(f"Action: {result['action']}")
    print(f"Plan:\n{result['plan']}")
    
    # Summary
    print("\n--- Summary ---")
    summary = federation.run("Healthcare federation")
    print(f"Members: {summary['members']}")
    print(f"Protocols: {summary['protocols']}")
    print(f"Resource Exchanges: {summary['resource_exchanges']}")

    

Agentic Architecture Pattern: Federated Agent

Pattern 35: 35 Federated Agent – Line-by-Line Explanation

Overview

Pattern 35: Federated Agent Pattern

Autonomous organizational units that maintain independence while coordinating through a federation. Each unit (federation member) retains local autonomy but adheres to federation protocols.

Code Structure Analysis

File Information

• Pattern Number: 35
• Pattern Name: 35 Federated Agent
• File Path: patterns\organizational\35_federated_agent.py
• Total Lines: 173

Detailed Code Explanation

Module Docstring

"""
Pattern 35: Federated Agent Pattern

Autonomous organizational units that maintain independence while coordinating through a federation.
Each unit (federation member) retains local autonomy but adheres to federation protocols.
"""

Explanation: This module docstring describes the purpose and key features of the 35 Federated Agent pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

FederationMember (Line 16)

Description: Autonomous organizational unit in federation.

FederationProtocol (Line 25)

Description: Shared rules for federation coordination.

FederatedAgentPattern (Line 32)

Description: Federated Agent Pattern – Autonomous units with coordinated governance.

Features: – Local autonomy for each member – Shared protocols and standards – Resource sharing agreements – Decentralized decision-making – Cross-federation collaboration

Methods:

__init__ (Line 44)

def __init__(self, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

add_member (Line 50)

def add_member(self, member):
    """Add a member to the federation."""

Purpose: Add a member to the federation.

Parameters: – self: Instance reference – member: Parameter for member

create_protocol (Line 55)

def create_protocol(self, protocol):
    """Create a federation-wide protocol."""

Purpose: Create a federation-wide protocol.

Parameters: – self: Instance reference – protocol: Parameter for protocol

sign_protocol (Line 61)

def sign_protocol(self, member_name, protocol_name):
    """Member signs agreement to follow protocol."""

Purpose: Member signs agreement to follow protocol.

Parameters: – self: Instance reference – member_name: Parameter for member_name – protocol_name: Parameter for protocol_name

share_resource (Line 69)

def share_resource(self, from_member, to_member, resource):
    """Share resource between federation members."""

Purpose: Share resource between federation members.

Parameters: – self: Instance reference – from_member: Parameter for from_member – to_member: Parameter for to_member – resource: Parameter for resource

coordinate_action (Line 83)

def coordinate_action(self, action, participating_members):
    """Coordinate action across multiple members."""

Purpose: Coordinate action across multiple members.

Parameters: – self: Instance reference – action: Parameter for action – participating_members: Parameter for participating_members

run (Line 112)

def run(self, scenario):
    """Execute federation scenario."""

Purpose: Execute federation scenario.

Parameters: – self: Instance reference – scenario: Parameter for scenario

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = FederationMember(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities