AI Agent Design Patterns: Beyond Simple Chains

Most developers start with simple prompt chains: input → LLM → output. This works for basic tasks, but real-world applications demand more sophisticated approaches. Modern AI agents use four fundamental patterns that enable complex reasoning, tool interaction, and collaborative problem-solving.

Pattern 1: Router Agents

Problem: Different queries need different processing approaches, but you don’t want to burden a single agent with every possible task.

Solution: Router agents analyze incoming requests and delegate to specialized sub-agents or tools.

from langchain.agents import AgentType, initialize_agent
from langchain.tools import BaseTool

class RouterAgent:
    def __init__(self):
        self.routes = {
            'data_analysis': DataAnalysisAgent(),
            'code_review': CodeReviewAgent(),
            'documentation': DocumentationAgent()
        }
    
    def route_request(self, query: str) -> str:
        # Classify query intent
        intent = self.classify_intent(query)
        agent = self.routes.get(intent)
        return agent.process(query)

When to use: Multiple distinct task types, need for specialized expertise, or when different tasks require different tool sets.

Pattern 2: Reflexion Agents

Problem: LLMs make mistakes and need mechanisms for self-correction and iterative improvement.

Solution: Reflexion agents include a feedback loop where they review their own work and refine their approach.

class ReflexionAgent:
    def solve_with_reflection(self, problem: str, max_iterations: int = 3):
        solution = self.initial_attempt(problem)
        
        for i in range(max_iterations):
            critique = self.self_critique(problem, solution)
            if self.is_satisfactory(critique):
                break
            solution = self.refine_solution(problem, solution, critique)
        
        return solution
    
    def self_critique(self, problem: str, solution: str) -> str:
        return self.llm.invoke(f"""
        Problem: {problem}
        Proposed Solution: {solution}
        
        Critique this solution. What are its weaknesses?
        What specific improvements would make it better?
        """)

When to use: Complex reasoning tasks, high accuracy requirements, or when initial attempts often need refinement.

Pattern 3: Tool-Using Agents

Problem: LLMs are powerful for reasoning but can’t interact with external systems, databases, or APIs directly.

Solution: Equip agents with tools that extend their capabilities beyond text generation.

from langchain.tools import Tool

def create_calculator_tool():
    return Tool(
        name="Calculator",
        description="Performs mathematical calculations",
        func=lambda x: eval(x)  # Use safely in production!
    )

def create_database_tool():
    return Tool(
        name="Database Query",
        description="Query customer database",
        func=execute_sql_query
    )

# Agent with tools
tools = [create_calculator_tool(), create_database_tool()]
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)

When to use: Need real-time data access, computation beyond LLM capabilities, or integration with existing systems.

Pattern 4: Multi-Agent Systems

Problem: Single agents become unwieldy for complex, multi-step processes that benefit from specialized expertise.

Solution: Deploy multiple agents that collaborate, each with specific roles and capabilities.

class MultiAgentSystem:
    def __init__(self):
        self.researcher = ResearchAgent()
        self.analyzer = AnalysisAgent()
        self.writer = WritingAgent()
    
    def process_research_request(self, topic: str) -> str:
        # Researcher gathers information
        research_data = self.researcher.gather_info(topic)
        
        # Analyzer processes findings
        insights = self.analyzer.extract_insights(research_data)
        
        # Writer creates final report
        report = self.writer.create_report(topic, insights)
        
        return report

When to use: Complex workflows, need for specialized expertise at each step, or when task parallelization can improve performance.

Pattern Selection Guide

Choose your pattern based on these criteria:

• Simple, single-domain tasks: Start with basic chains
• Multiple task types: Router pattern
• High accuracy needs: Reflexion pattern
• External system integration: Tool-using pattern
• Complex, multi-step workflows: Multi-agent pattern

Implementation Considerations

Performance: Router patterns reduce latency by avoiding unnecessary processing. Multi-agent systems can introduce coordination overhead.

Reliability: Reflexion patterns improve accuracy but increase token usage. Tool-using patterns inherit the reliability characteristics of external systems.

Cost: More complex patterns generally mean more LLM calls. Budget accordingly and implement caching where possible.

Next Steps

Start with the simplest pattern that meets your needs. You can always evolve toward more complex patterns as requirements grow. The next article in this series will dive deep into context engineering—how to manage memory and state in these more sophisticated agent architectures.

Focus on measuring what matters: task completion rates, user satisfaction, and system reliability. The fanciest pattern means nothing if it doesn’t solve real problems better than simpler alternatives.