Skip to content

Instantly share code, notes, and snippets.

@ctalladen78

ctalladen78/agentic_design_patterns.md

Last active November 25, 2025 17:55
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save ctalladen78/8b90d12c972a9d0f5b08cbf3284766d7 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save ctalladen78/8b90d12c972a9d0f5b08cbf3284766d7 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
agentic_design_patterns.md

Antonio Gulli’s "Agentic Design Patterns" provides 21 structured patterns for building autonomous AI agents, combining practical frameworks, hands-on code, and multi-framework deployment guides for real-world applications [^1^] [^3^].

Overview

Antonio Gulli, a Senior Director and Distinguished Engineer at Google with over 30 years of experience in AI, Search, and Cloud technologies, authored "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems". This 427-page guide offers a systematic approach to constructing intelligent, goal-oriented AI systems and emphasizes transitioning from raw AI capabilities to robust, reliable applications [^3^] [^5^]. The book defines agentic AI systems as autonomous entities capable of perceiving their environment, making informed decisions, and executing tasks without constant human supervision. It bridges the gap between language models’ raw capabilities and practical agent deployment for complex, multi-step problem-solving [^1^] [^6^].

Structure and Key Patterns

The publication is organized into four major parts plus appendices, with each chapter dedicated to a single agentic pattern [^9^]: Part One – Core Execution Patterns

  • Prompt Chaining – Sequential task decomposition
  • Routing – Dynamic decision-making and workflow direction
  • Parallelization – Concurrent processing of multiple tasks
  • Reflection – Self-assessment and iterative improvement
  • Tool Use – Integration of external APIs and capabilities
  • Planning – Strategic goal decomposition and scheduling
  • Multi-Agent Systems – Coordinated, collaborative agents

Part Two – Memory and Learning Capabilities

  • Memory Management – Persisting short-term and long-term state
  • Learning and Adaptation – Updating behavior based on experience
  • Model Context Protocol (MCP) – Standardized context interfaces
  • Goal Setting and Monitoring – Task prioritization and progress tracking

Part Three – Production Patterns

  • Exception Handling and Recovery – Robust error management
  • Human-in-the-Loop – Collaboration between agents and humans
  • Knowledge Retrieval (RAG) – Accessing structured and unstructured data

Part Four – Enterprise Patterns

  • Inter-Agent Communication (A2A) – Agent networking and interaction
  • Resource-Aware Optimization – Efficient use of computational resources
  • Reasoning Techniques – Advanced decision-making logic
  • Guardrails/Safety Patterns – Ensuring safe and aligned operation
  • Evaluation and Monitoring – Performance measurement and analysis
  • Prioritization – Task and objective management
  • Exploration and Discovery – Autonomous learning and adaptation

The appendices provide advanced prompting techniques, workflow integration, and technical glossaries [^9^].

Implementation Frameworks

Gulli demonstrates these patterns across three major development frameworks:

  • LangChain and LangGraph – For constructing complex sequential workflows
  • CrewAI – Facilitates orchestration and multi-agent coordination
  • Google Agent Developer Kit (ADK) – Full-cycle evaluation, deployment, and management of AI agents [^3^] [^5^]

Hands-on examples and Jupyter notebooks accompany each pattern, enabling immediate experimentation and practical application [^9^].

Practical Applications and UX

Agentic design patterns are not just theoretical; they influence user experience, human-AI collaboration, and enterprise automation [^8^]:

  • Context-Aware Adaptation – Adjust agent actions based on user behavior
  • Confidence-Signaled Suggestions – Display reliability of AI recommendations
  • Recovery Loops and Safe-Stop – Enable error resilience and human override
  • Multi-Agent Coordination – Parallel task execution with feedback mechanisms

Industries like marketing, analytics, and customer support benefit from these patterns by deploying intelligent agents for autonomous campaign management, data analysis, and interactive customer engagement [^1^] [^8^].

Significance

This guide represents a comprehensive framework for building dependable AI agents. By leveraging standardized design patterns, developers can:

  • Quickly develop maintainable and robust AI systems
  • Enable extended-task reasoning and multi-agent collaboration
  • Integrate safety, monitoring, and human oversight mechanisms
  • Apply best practices across different technical environments without reinventing solutions [^1^] [^6^]

The publication is also publicly accessible for educational and practitioner use, emphasizing hands-on implementation over purely theoretical approaches [^1^] [^9^]. In summary, Antonio Gulli’s "Agentic Design Patterns" equips developers with the knowledge, code, and architectural guidance needed to design, implement, and manage autonomous AI systems effectively in both experimental and enterprise settings [^3^] [^6^].

Source(s):

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment