Agentic Design Patterns: The Complete Guide to Building Intelligent AI Systems

Why This Series Exists

The brands that will dominate the next decade are the ones building intelligent systems today — not just using AI, but architecting agents that can autonomously solve problems, retrieve knowledge, coordinate with other agents, and improve themselves over time.

This series distills the 21 core design patterns from Antonio Gulli's Agentic Design Patterns into actionable, enterprise-focused posts. Each one gives you the concept, the code, and the competitive context.

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The Series

Foundations

1. What Makes an AI System an Agent? The Foundation of Agentic Design — The five-step loop every agent follows, the four levels of agent complexity, and five hypotheses for where this is all heading.

Core Orchestration Patterns

1. Prompt Chaining: Building Reliable AI Agent Workflows — Break complex tasks into sequential, manageable steps. The foundational pattern everything else builds on.

1. Routing and Parallelization: Scaling AI Agent Orchestration — Dynamic decision-making and concurrent execution. How agents choose the right path and do multiple things at once.

1. Reflection and Adaptation: How AI Agents Learn From Their Own Output — Self-correction feedback loops that turn first drafts into polished results.

External Integration Patterns

1. Tool Use in AI Agents: Function Calling and Beyond — How agents break out of the text bubble to call APIs, query databases, and trigger real-world actions.

1. Model Context Protocol (MCP): The New Standard for Agent-Tool Integration — The emerging protocol that standardizes how agents connect to tools and data sources.

1. RAG for AI Agents: Retrieval-Augmented Generation Done Right — Ground your agents in factual, up-to-date knowledge with retrieval-augmented generation.

Planning & Reasoning

1. AI Agent Planning: How Intelligent Systems Decide What to Do Next — Task decomposition, plan-and-execute strategies, and how agents turn goals into action sequences.

1. Reasoning Techniques for AI Agents: Chain-of-Thought to Tree-of-Thought — The reasoning architectures that separate toy demos from production-grade agents.

Collaboration & Communication

1. Multi-Agent Systems: Orchestrating Teams of AI Agents — How specialized agents work in concert to tackle objectives no single agent could handle.

1. Agent-to-Agent Communication: How A2A Enables Agent Interoperability — The protocol layer that lets agents from different systems discover, negotiate, and collaborate.

Memory & State

1. Memory Management for AI Agents: Short-Term, Long-Term, and Beyond — How agents remember context across interactions and build persistent knowledge.

Reliability & Safety

1. AI Guardrails and Safety: Building Trustworthy Agentic Systems — The safety patterns that make the difference between a demo and an enterprise deployment.

1. Exception Handling and Human-in-the-Loop: Making AI Agents Resilient — Graceful failure recovery and knowing when to bring a human into the loop.

Operations & Optimization

1. Monitoring AI Agents: Goal Setting, Evaluation, and Prioritization — How to set objectives, track performance, and prioritize what your agents work on.

1. Resource-Aware AI Agents: Optimization and Exploration Strategies — Cost-conscious agent design and intelligent exploration of solution spaces.

Perspectives & Resources

1. Industry Leaders on Agentic AI: Perspectives from Google and Goldman Sachs — Saurabh Tiwary (VP, Google CloudAI) and Marco Argenti (CIO, Goldman Sachs) on the future of intelligent systems.

1. The Agentic AI Toolkit: Frameworks, Environments, and CLI Agents — A practical tour of LangChain, CrewAI, Google ADK, AgentSpace, and the tools powering agent development today.

1. The Definitive Glossary of Agentic AI: 100+ Terms Explained — Every term you need to navigate the agentic AI landscape, defined and cross-referenced.

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About the Book

Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems by Antonio Gulli is published by Springer and covers 21 design patterns with hands-on code examples using LangChain, CrewAI, and Google ADK. All royalties are donated to Save the Children.

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