Agent Architecture

Basic Agent Architecture

Core Components

┌─────────────────────────────────────┐
│         Agent System                │
├─────────────────────────────────────┤
│  ┌─────────┐  ┌─────────┐          │
│  │   LLM   │  │ Memory  │          │
│  │ (Brain) │  │(Memory) │          │
│  └────┬────┘  └────┬────┘          │
│       │            │               │
│       └──────┬─────┘               │
│              │                     │
│       ┌──────▼──────┐              │
│       │  Planner    │              │
│       │ (Planning)  │              │
│       └──────┬──────┘              │
│              │                     │
│       ┌──────▼──────┐              │
│       │  Executor   │              │
│       │ (Execution) │              │
│       └──────┬──────┘              │
│              │                     │
│       ┌──────▼──────┐              │
│       │   Tools     │              │
│       │  (Toolset)  │              │
│       └─────────────┘              │
└─────────────────────────────────────┘

Component Description

1. LLM (Brain)

Responsibilities:

• Understanding user requirements
• Planning execution steps
• Generating action instructions
• Processing execution results

Example:

class LLM:
    def understand(self, user_input):
        """Understand user requirements"""
        pass

    def plan(self, goal):
        """Plan execution steps"""
        pass

    def decide(self, context):
        """Make decisions"""
        pass

    def generate(self, instruction):
        """Generate output"""
        pass

2. Memory

Responsibilities:

• Storing historical information
• Maintaining current state
• Providing information retrieval

Types:

• Short-term memory: Current conversation context
• Long-term memory: Persistent storage
• Working memory: Temporary task information

Example:

class Memory:
    def __init__(self):
        self.short_term = []  # Short-term memory
        self.long_term = {}   # Long-term memory
        self.working = {}     # Working memory

    def store(self, key, value, memory_type='short'):
        """Store information"""
        pass

    def retrieve(self, key):
        """Retrieve information"""
        pass

    def update(self, key, value):
        """Update information"""
        pass

3. Planner

Responsibilities:

• Decomposing complex tasks
• Generating execution plans
• Adjusting planning strategies

Example:

class Planner:
    def decompose(self, task):
        """Decompose task"""
        pass

    def plan(self, subtasks):
        """Generate execution plan"""
        pass

    def adjust(self, plan, feedback):
        """Adjust plan"""
        pass

4. Executor

Responsibilities:

• Executing specific actions
• Calling tools
• Processing execution results

Example:

class Executor:
    def execute(self, action):
        """Execute action"""
        pass

    def call_tool(self, tool_name, params):
        """Call tool"""
        pass

    def handle_result(self, result):
        """Handle result"""
        pass

5. Tools

Responsibilities:

• Providing specific functionality
• Executing specific tasks
• Returning execution results

Types:

• Code execution tools
• File operation tools
• Network request tools
• Database tools
• Other specialized tools

Example:

class Tools:
    def __init__(self):
        self.tools = {}

    def register(self, name, tool):
        """Register tool"""
        self.tools[name] = tool

    def execute(self, name, params):
        """Execute tool"""
        return self.tools[name].execute(params)

Tool Calling Mechanism

Basic Process

1. LLM analyzes requirements
   ↓
2. Determines need to call tool
   ↓
3. Generates tool call instruction
   ↓
4. Executor executes tool
   ↓
5. Gets tool return result
   ↓
6. LLM processes result
   ↓
7. Decides next action

Example: Search Information

# 1. LLM analyzes requirements
user_input = "Search for the latest developments in large language models in 2023"

# 2. LLM determines need for search tool
tool_decision = {
    "tool": "search",
    "params": {
        "query": "latest developments in large language models 2023"
    }
}

# 3. Executor executes tool
result = executor.call_tool("search", tool_decision["params"])

# 4. LLM processes result
response = llm.generate(f"Answer the question based on the following search results: {result}")

Tool Call Format

JSON Format Example:

{
    "tool": "search",
    "params": {
        "query": "latest developments in large language models 2023",
        "num_results": 10
    }
}

Text Format Example:

Tool: search
Parameters:
- query: "latest developments in large language models 2023"
- num_results: 10

Planning and Execution

Task Decomposition

Principles:

• Decompose large tasks into small tasks
• Each small task should be independently completable
• Clear dependencies between small tasks

Example:

Large task: Develop a user authentication system

Decomposed into:
1. Design database model
2. Implement user registration functionality
3. Implement user login functionality
4. Add password encryption
5. Implement session management
6. Write test cases
7. Generate API documentation

Execution Plan

Format:

plan = [
    {
        "task": "Design database model",
        "tool": "code_generator",
        "params": {...}
    },
    {
        "task": "Implement user registration functionality",
        "tool": "code_generator",
        "params": {...}
    },
    ...
]

Execution Process

class Agent:
    def execute_plan(self, plan):
        results = []
        for step in plan:
            # Execute each step
            result = self.executor.execute(step)
            results.append(result)

            # Check if plan needs adjustment
            if self.needs_adjustment(result):
                plan = self.planner.adjust(plan, result)

        return results

Reflection and Improvement

Reflection Mechanism

Purpose:

• Evaluate execution results
• Identify problems and errors
• Propose improvement suggestions

Example:

class Reflector:
    def reflect(self, action, result):
        """Reflect on action and result"""
        issues = self.identify_issues(action, result)
        suggestions = self.generate_suggestions(issues)
        return {
            "issues": issues,
            "suggestions": suggestions
        }

Improvement Mechanism

Purpose:

• Improve based on reflection results
• Optimize execution strategies
• Update knowledge base

Example:

class Improver:
    def improve(self, plan, reflection):
        """Improve plan based on reflection results"""
        improved_plan = self.apply_suggestions(plan, reflection)
        return improved_plan

Multi-Agent Collaboration

Collaboration Patterns

1. Hierarchical Collaboration

Master Agent
    ↓
    ├─ Agent 1 (Subtask 1)
    ├─ Agent 2 (Subtask 2)
    └─ Agent 3 (Subtask 3)

2. Peer Collaboration

Agent 1 ←→ Agent 2
    ↑         ↓
    └─ Agent 3 ┘

3. Specialized Collaboration

Code Agent ←→ Test Agent ←→ Doc Agent

Collaboration Example

class MultiAgentSystem:
    def __init__(self):
        self.agents = {
            "code": CodeAgent(),
            "test": TestAgent(),
            "doc": DocAgent()
        }

    def execute_task(self, task):
        # Assign tasks
        subtasks = self.assign(task)

        # Execute in parallel
        results = []
        for agent_name, subtask in subtasks.items():
            result = self.agents[agent_name].execute(subtask)
            results.append(result)

        # Integrate results
        return self.integrate(results)

Practical Application Case

Case: Code Development Agent

class CodeAgent:
    def __init__(self):
        self.llm = LLM()
        self.memory = Memory()
        self.planner = Planner()
        self.executor = Executor()
        self.tools = Tools()

        # Register tools
        self.tools.register("code_gen", CodeGenerator())
        self.tools.register("code_test", CodeTester())
        self.tools.register("file_ops", FileOperations())

    def develop(self, requirement):
        # 1. Understand requirements
        understanding = self.llm.understand(requirement)

        # 2. Plan steps
        plan = self.planner.plan(understanding)

        # 3. Execute plan
        results = []
        for step in plan:
            result = self.executor.execute(step)
            results.append(result)

            # 4. Reflect and improve
            reflection = self.reflect(step, result)
            if reflection["issues"]:
                plan = self.improve(plan, reflection)

        # 5. Return results
        return self.finalize(results)

Summary

Agent architecture is key to building intelligent systems:

Key Points:

• ✅ Core components: LLM + Memory + Planner + Executor + Tools
• ✅ Tool calling mechanism connects LLM and external tools
• ✅ Separation of planning and execution improves system reliability
• ✅ Reflection and improvement mechanisms continuously optimize
• ✅ Multi-Agent collaboration extends system capabilities

Best Practices:

1. Clearly define component responsibilities
2. Design flexible tool calling mechanisms
3. Implement effective planning strategies
4. Establish reflection and improvement mechanisms
5. Support multi-Agent collaboration

Remember:

• Architecture design should consider scalability
• Tool calling should be safe and reliable
• Planning should be flexible and adjustable
• Reflection should be continuous
• Collaboration should be efficient and orderly

Understanding Agent architecture helps design and implement more powerful AI systems.

Next Steps

• Agent Case Studies - Learn about practical Agent applications
• Common Agent Patterns - Learn about common Agent design patterns