Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems

Course Overview

"Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems" is a comprehensive AI agent system design guide written by Antonio Gulli, Engineering Director at Google's Office of the CTO. This is a 406-page practical eBook focused on design patterns for building intelligent AI agent systems.

Author Introduction

Antonio Gulli is a Senior Director at Google, currently serving as Engineering Director in the Office of the CTO. He has over 30 years of relevant experience and is a well-known figure in the industry with deep expertise in AI, Search, and Cloud technologies.

Course Features

1. Free and Open Resource

• The author writes this book in an open environment, freely accessible to anyone for review and suggestions
• No paywall, registration, or other restrictions
• GitHub repository provides complete PDF and Jupyter Notebook code examples

2. Practice-Oriented

• Each chapter focuses on a specific agent design pattern
• Provides detailed pattern overview
• Includes practical applications and use cases
• Contains one or more hands-on code examples
• Key takeaways at the end of each chapter for quick review

3. Cross-Framework Support

To provide a concrete "canvas" for code examples, this guide utilizes three prominent agent development frameworks:

• LangChain and LangGraph: Offers flexible ways to build complex operational sequences
• Crew AI: Provides a structured framework for orchestrating multiple agents
• Google Agent Developer Kit (Google ADK): Offers tools for building, evaluating, and deploying agents

By showcasing examples across these tools, readers gain a broad understanding of how these patterns can be applied in any technical environment.

Core Content: 21 Design Patterns

This book covers 21 essential agent design patterns, from foundational concepts to advanced topics:

Foundational Patterns

• Prompt Chaining: Sequential prompt execution for complex multi-step tasks
• Routing: Intelligent request classification and routing to appropriate handlers
• Tool Use: Strategic external tool integration and management

Memory and Learning Patterns

• Memory Management: Contextual continuity through intelligent information storage
• Learning Adapter: Dynamic improvement through experience and feedback

Planning and Collaboration Patterns

• Planner: Structured task decomposition with dependency management
• Multi-Agent Collaboration: Collaborative problem-solving through agent coordination
• Agent Communication: Structured communication infrastructure for agent coordination

Quality Assurance Patterns

• Self-Correction: Systematic error management and system resilience
• Human Validator: Strategic human oversight integration for quality control
• Exception Handler: Systematic error management and system resilience

Advanced Patterns

• RAG Retriever: Dynamic external knowledge access during response generation
• MCP Integrator: Standardized communication with external resources
• Resource Optimizer: Dynamic resource monitoring and optimization
• Safety Guardian: Comprehensive safety mechanisms for acceptable operation bounds
• Evaluator: Comprehensive performance assessment and system health tracking
• Prioritizer: Intelligent task ranking and scheduling based on multiple criteria
• Explorer: Systematic investigation of unknown environments for knowledge acquisition
• Reasoning Engine: Systematic logical inference and structured problem-solving
• Goal Monitor: Executive function providing direction and accountability

Learning Objectives

Through this course, you will be able to:

1. Understand theoretical foundations of agent design patterns: Master core concepts and principles behind each pattern
2. Gain practical skills: Implement these 21 essential patterns
3. Build intelligent systems: Construct more intelligent, capable, and autonomous systems on your chosen development canvas
4. Apply best practices: Use battle-tested solutions to address common design and implementation challenges in the agentic domain
5. Improve system quality: Enhance the structure, maintainability, reliability, and efficiency of the agents you build

Course Structure

Chapter Organization

• Each chapter focuses on a single agent pattern
• Chapters build upon each other but can also be used as a reference handbook
• Jump to patterns that address your specific challenges

Content Composition

Each chapter includes:

1. Pattern Overview: Detailed introduction to the pattern's definition and applicable scenarios
2. Practical Applications: Showcases real-world use cases
3. Code Examples: Provides runnable implementation code
4. Key Takeaways: Summarizes core knowledge points

Technical Requirements

Development Frameworks

# LangChain example
from langchain import PromptTemplate, LLMChain

# Crew AI example
from crewai import Agent, Task, Crew

# Google ADK example
from google_adk import Agent, Tool

Target Audience

• AI/ML developers
• Software engineers
• System architects
• Technical personnel who want to build intelligent agent systems
• Researchers interested in autonomous AI systems

Core Philosophy

The Importance of Design Patterns

Agentic design patterns are not rigid rules, but rather battle-tested templates or blueprints that offer proven approaches to standard design and implementation challenges in the agentic domain.

Value of Patterns

By applying these design patterns, you gain:

• Structure: Clear agent logic
• Maintainability: Easy to understand and modify code
• Robustness: Proven reliable solutions
• Efficiency: Optimized system performance
• Common Language: Standard terminology for team collaboration

Learning Path from Basic to Advanced

1. Foundation Stage:

   • Understand basic patterns like Prompt Chaining and Routing
   • Learn Tool Use and basic workflow management

2. Intermediate Stage:

   • Master Memory Management and RAG Retrieval
   • Implement Planning and Multi-Agent Collaboration

3. Advanced Stage:

   • Explore Self-Correction and Learning Adapter
   • Implement enterprise-grade patterns like Safety Guardian and Resource Optimizer

GitHub Repository Resources

Repository Contents

• PDF Document: Complete 424-page eBook
• Jupyter Notebooks: Hands-on code examples for each pattern
• Code Examples: Implementations across multiple frameworks

Repository Statistics

• ⭐ Stars: 1.1k+
• 🔱 Forks: 400+
• 📝 Language: Jupyter Notebook

Practical Application Value

For Developers

• Provides directly applicable code examples
• Learn industry best practices
• Accelerate AI agent development process

For Technical Leaders

• Understand the architectural logic of AI systems
• Avoid common AI pitfalls: hallucinations, context loss, unreliable performance
• Provide standardized development approaches for teams

For Organizations

• Unlock the true value of AI systems
• Build maintainable and scalable AI solutions
• Reduce technical risks in AI projects

Key Technical Topics

Advanced Prompting Techniques

• Clear and detailed prompt writing
• Using positive and negative examples
• Encouraging step-by-step reasoning
• Specifying desired length or output format

Memory Management

• Contextual continuity
• Intelligent information storage
• Long-term and short-term memory

RAG (Retrieval-Augmented Generation)

• Dynamic knowledge access
• External information integration
• Improved response accuracy

Inter-Agent Communication

• Coordination mechanisms
• Message passing protocols
• Collaborative workflows

Tool Use

• External API integration
• Function calling
• Tool selection strategies

Safety and Quality Assurance

Safety Patterns

• Safety Guardian: Ensures operations within acceptable bounds
• Exception Handler: Systematic error management
• Human Validator: Human review for critical decisions

Quality Control

• Evaluator: Performance assessment
• Self-Correction: Automatic error correction
• Goal Monitor: Goal alignment checks

Recommended Learning Resources

Official Resources

• GitHub Repository: https://github.com/sarwarbeing-ai/Agentic_Design_Patterns
• Springer Publication Link: https://link.springer.com/book/10.1007/978-3-032-01402-3
• Amazon Purchase Link: https://www.amazon.com/Agentic-Design-Patterns-Hands-Intelligent/dp/3032014018

Supplementary Learning

• Anthropic Prompt Engineering Documentation: https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/overview
• LangChain Official Documentation
• Crew AI Documentation
• Google ADK Documentation

Summary

"Agentic Design Patterns" is a comprehensive, practical, and free resource that provides a systematic approach to building intelligent AI agent systems. Through 21 carefully designed patterns, from foundational to advanced, with cross-framework code examples, this book provides developers, architects, and technical leaders with the knowledge and tools needed to build reliable, maintainable, and efficient AI systems.

Whether you're just starting with AI agent development or looking to improve existing systems, this book offers valuable insights and practical guidance. Its open and free nature makes it an invaluable resource for the AI community.