Architectural Patterns

1. Content

1.1. Introduction

Architectural patterns in agentic systems define how autonomous agents interact, manage state, and accomplish tasks. This section explores core patterns for task execution, memory management, and agent coordination.

1.2. Main Content

1.2.1. Core Pattern Categories

1.2.1.1. Manager-Coordinator-Executor (MCE)

Components

Manager
High-level task planning and resource allocation

Coordinator
Task decomposition and workflow management

Executor
Specialized task execution and direct actions

Implementation Examples

class Manager:
    def allocate_task(self, task):
        coordinator = self.select_coordinator(task)
        return coordinator.process(task)

class Coordinator:
    def process(self, task):
        subtasks = self.decompose(task)
        executors = [self.assign_executor(t) for t in subtasks]
        return self.monitor_execution(executors)

class Executor:
    def execute(self, subtask):
        result = self.perform_action(subtask)
        return self.validate_result(result)

1.2.1.2. Hand-off Pattern

Key Components

Context Storage
Maintains task state and history

Transfer Protocol
Defines hand-off mechanics

State Validation
Ensures context integrity

Implementation Example

class ContextManager:
    def store_context(self, context_id, state):
        return self.vector_store.upsert({
            'id': context_id,
            'state': state,
            'timestamp': datetime.now(),
            'metadata': self.get_metadata()
        })

class TaskHandoff:
    def transfer(self, from_agent, to_agent, task, context_id):
        context = self.context_manager.get_context(context_id)
        return HandoffProtocol(
            source=from_agent,
            target=to_agent,
            task=task,
            context=context
        ).execute()

1.2.1.3. Memory-Centric Pattern

Components

Short-term Memory
Active context and current task state

Long-term Memory
Historical patterns and learned behaviors

Context Manager
Memory orchestration and retrieval

Implementation Example

class MemorySystem:
    def __init__(self):
        self.stm = ShortTermMemory()
        self.ltm = LongTermMemory()
        self.context = ContextManager()
    
    def process_task(self, task):
        current_context = self.stm.get_active_context()
        similar_patterns = self.ltm.find_similar(task)
        execution_plan = self.context.create_plan(
            task, current_context, similar_patterns
        )
        return self.execute_with_memory(execution_plan)

1.2.2. Pattern Integration

1.2.2.1. Cross-Pattern Communication

1.2.2.2. Implementation Considerations

State Management
- Context preservation
- Memory efficiency
- Pattern storage
Error Handling
- Recovery mechanisms
- State rollback
- Pattern adaptation
Resource Optimization
- Memory allocation
- Compute distribution
- Context pruning

1.3. Summary

Key architectural patterns enable:

Structured task decomposition and execution
Efficient context management and transfer
Integration of short and long-term memory
Scalable agent coordination
Robust error handling and recovery

1.4. Section References

Design Patterns: Elements of Reusable Agent Architectures
Microsoft Magentic-One Architecture Documentation
Vector Database Integration Patterns
Memory Management in Distributed Systems
Pattern implementations:
- Manager-Coordinator-Executor
  - Anthropic's Agent System
  - OpenAI's Assistant API
- Hand-off Systems
  - LangChain's Agent Framework
  - Auto-GPT Architecture
- Memory Patterns
  - MemGPT
  - Vector Databases