🤖 If you're looking to build agentic GenAI applications

Agentic Application Design Patterns: Building Intelligent AI Systems

Curiosity: How do we design agentic GenAI applications? What patterns enable agents to think, plan, and collaborate effectively?

Agentic AI is evolving rapidly, opening possibilities for a new paradigm of applications. When developing agentic applications, choosing the right design patterns is crucial for success.

Design Patterns Overview

graph TB
    A[Agentic Design Patterns] --> B[Reflection]
    A --> C[Tool Use]
    A --> D[Planning]
    A --> E[Multi-Agent Collaboration]
    
    B --> B1[Self-Review]
    B --> B2[Improvement]
    
    C --> C1[Web Search]
    C --> C2[Code Execution]
    C --> C3[Functions]
    
    D --> D1[Multi-Step Plans]
    D --> D2[Goal Achievement]
    
    E --> E1[Task Division]
    E --> E2[Collaboration]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style C fill:#d4edda
    style D fill:#f8d7da
    style E fill:#e7d4f8

The 4 Key Design Patterns

Pattern	Description	Use Case	Complexity
Reflection	LLM reviews its own work	Quality improvement	⭐ Low
Tool Use	LLM equipped with tools	Information gathering	⭐⭐ Medium
Planning	Multi-step plan execution	Complex tasks	⭐⭐⭐ High
Multi-Agent	Multiple agents collaborate	Large-scale problems	⭐⭐⭐⭐ Very High

1. Reflection Pattern

Retrieve: The LLM reviews its own work to identify ways to improve.

How It Works:

• Agent generates initial output
• Reviews and critiques its own work
• Identifies improvements
• Refines output

Architecture:

graph LR
    A[Input] --> B[Generate Output]
    B --> C[Review Output]
    C --> D{Needs<br/>Improvement?}
    D -->|Yes| B
    D -->|No| E[Final Output]
    
    style B fill:#e1f5ff
    style C fill:#fff3cd
    style E fill:#d4edda

Example:

# Reflection pattern
def reflection_agent(query):
    # Initial generation
    output = llm.generate(query)
    
    # Review
    critique = llm.generate(
        f"Review this output and suggest improvements:\n{output}"
    )
    
    # Refine if needed
    if "improve" in critique.lower():
        output = llm.generate(
            f"Original: {output}\nImprovements: {critique}\nGenerate improved version"
        )
    
    return output

Benefits:

• ✅ Self-improvement
• ✅ Quality enhancement
• ✅ Error correction
• ✅ Iterative refinement

Article: Reflection Pattern

2. Tool Use Pattern

Innovate: The LLM is equipped with tools to gather information, take action, or process data.

Tools Available:

• 🔍 Web search
• 💻 Code execution
• 📊 Data processing
• 🔧 API calls
• 📁 File operations

Architecture:

graph TB
    A[User Query] --> B[Agent]
    B --> C{Need Tool?}
    C -->|Yes| D[Select Tool]
    D --> E[Execute Tool]
    E --> F[Process Result]
    F --> B
    C -->|No| G[Generate Response]
    
    style B fill:#e1f5ff
    style D fill:#fff3cd
    style G fill:#d4edda

Example:

# Tool use pattern
tools = {
    "web_search": web_search_tool,
    "code_exec": code_execution_tool,
    "calculator": calculator_tool
}

def tool_use_agent(query):
    # Agent decides which tools to use
    tool_selection = llm.select_tools(query, available_tools=tools)
    
    results = []
    for tool_name in tool_selection:
        tool = tools[tool_name]
        result = tool.execute(query)
        results.append(result)
    
    # Generate response using tool results
    return llm.generate(query, context=results)

Benefits:

• ✅ Extended capabilities
• ✅ Real-time information
• ✅ Action execution
• ✅ Data processing

Article: Tool Use Pattern

3. Planning Pattern

Retrieve: The LLM devises and follows a multi-step plan to achieve a goal.

Process:

1. Analyze goal
2. Create step-by-step plan
3. Execute plan steps
4. Monitor progress
5. Adjust if needed

Architecture:

graph TB
    A[Goal] --> B[Create Plan]
    B --> C[Step 1]
    C --> D[Step 2]
    D --> E[Step N]
    E --> F{Goal<br/>Achieved?}
    F -->|No| B
    F -->|Yes| G[Complete]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style G fill:#d4edda

Example:

# Planning pattern
def planning_agent(goal):
    # Create plan
    plan = llm.generate(
        f"Create a step-by-step plan to achieve: {goal}"
    )
    
    steps = parse_plan(plan)
    
    # Execute plan
    results = []
    for step in steps:
        result = llm.execute_step(step, context=results)
        results.append(result)
        
        # Check if goal achieved
        if check_goal_achieved(goal, results):
            break
    
    return compile_results(results)

Use Cases:

• Essay writing (outline → research → draft)
• Complex problem solving
• Multi-step workflows
• Project management

Article: Planning Pattern

4. Multi-Agent Collaboration Pattern

Innovate: Multiple AI agents work together, dividing tasks and discussing ideas.

Benefits:

• ✅ Task specialization
• ✅ Better solutions
• ✅ Parallel processing
• ✅ Collective intelligence

Architecture:

graph TB
    A[Task] --> B[Coordinator]
    B --> C[Agent 1]
    B --> D[Agent 2]
    B --> E[Agent N]
    
    C --> F[Results 1]
    D --> G[Results 2]
    E --> H[Results N]
    
    F --> I[Discussion]
    G --> I
    H --> I
    I --> J[Final Solution]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style I fill:#d4edda
    style J fill:#f8d7da

Example:

# Multi-agent collaboration
agents = {
    "researcher": ResearchAgent(),
    "writer": WriterAgent(),
    "editor": EditorAgent()
}

def multi_agent_system(task):
    # Coordinator divides task
    subtasks = coordinator.divide_task(task, agents)
    
    # Agents work in parallel
    results = {}
    for agent_name, subtask in subtasks.items():
        agent = agents[agent_name]
        results[agent_name] = agent.process(subtask)
    
    # Discussion and synthesis
    final_result = coordinator.synthesize(results)
    return final_result

Use Cases:

• Complex research projects
• Content creation teams
• Software development
• Problem-solving teams

Article: Multi-Agent Collaboration

Pattern Selection Guide

graph TD
    A[Agentic Application Need] --> B{Task Complexity?}
    B -->|Simple| C[Reflection]
    B -->|Medium| D[Tool Use]
    B -->|Complex| E[Planning]
    B -->|Very Complex| F[Multi-Agent]
    
    C --> C1[Self-Review]
    D --> D1[External Tools]
    E --> E1[Multi-Step]
    F --> F1[Collaboration]
    
    style A fill:#e1f5ff
    style C fill:#fff3cd
    style D fill:#d4edda
    style E fill:#f8d7da
    style F fill:#e7d4f8

Combining Patterns

Innovate: Patterns can be combined for more powerful systems.

Example Combinations:

• Reflection + Tool Use: Agent uses tools, then reflects on results
• Planning + Multi-Agent: Multiple agents execute a coordinated plan
• All Patterns: Comprehensive agentic system

Key Takeaways

Retrieve: Four key design patterns—Reflection, Tool Use, Planning, and Multi-Agent Collaboration—provide frameworks for building agentic GenAI applications.

Innovate: By understanding and applying these patterns, you can design intelligent agentic systems that think, plan, use tools, and collaborate to solve complex problems.

Curiosity → Retrieve → Innovation: Start with curiosity about agentic design, retrieve insights from these patterns, and innovate by combining them to build powerful agentic applications.

Complete Blog Series: https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/?ref=dl-staging-website.ghost.io

Next Steps:

• Study each pattern in detail
• Choose patterns for your use case
• Implement and test
• Combine patterns as needed