Agentic Design Patterns Every Developer Should Know

Antonio Gulli and Mauro Sauco wrote one of the best practical guides to agentic design patterns I have come across — "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems." I forked the companion repo on GitHub because these patterns are the ones I keep coming back to when building agents for real enterprise use cases.

My fork is at github.com/GGCryptoh/agentic-design-patterns, based on the original by Mathews-Tom.

Why Patterns Matter

AI agents are not chatbots with extra steps. They are autonomous systems that make decisions, take actions, and handle failures. Without disciplined patterns, they become unpredictable, expensive, and dangerous.

The patterns in the book are not new inventions — they are borrowed from distributed systems, workflow engines, and game theory. What Gulli and Sauco did well is organize them into a practical taxonomy that developers can actually apply.

The Patterns I Use Most

1. ReAct (Reason + Act)

The agent thinks before it acts, observes the result, and adjusts. This is the foundational pattern. If your agent is not doing this, it is not an agent — it is a script.

In practice, this is the pattern that separates a useful agent from a liability. When I build agents for PE portfolio companies, the ReAct loop is what allows the agent to handle edge cases gracefully instead of failing silently.

2. Tool Use

The agent has access to external tools — APIs, databases, calculators — and decides which to use based on the task. This is where agents become useful. They can take real actions, not just generate text.

The key lesson from implementing this pattern: keep the tool set small and well-defined. An agent with 50 tools is slower and less reliable than one with 5 tools that cover the critical path.

3. Reflection

The agent evaluates its own output before presenting it. "Is this answer correct? Is it complete? Does it address the user's actual question?" Self-critique is the difference between a demo and a production system.

This pattern is chronically underused. Most agent implementations skip reflection entirely, which is why they produce confident-sounding garbage. Adding a reflection step increases latency by a few seconds but dramatically improves output quality.

4. Planning

The agent decomposes complex tasks into subtasks, executes them in order, and handles dependencies. This is borrowed directly from task scheduling in distributed systems.

5. Multi-Agent Collaboration

Multiple specialized agents work together on a task. One agent researches, another writes, a third reviews. This mirrors how high-performing human teams work.

This pattern is powerful but easy to over-engineer. Start with two agents (worker + reviewer) before building a committee of six.

6. Memory

The agent maintains context across interactions — both short-term (conversation) and long-term (learned preferences, past decisions). Without memory, every interaction starts from zero.

How I Apply These in Enterprise

The book gives you the theory. Here is how the patterns map to real enterprise use cases:

• ReAct + Tool Use — customer support agents that look up account data, check policies, and draft responses. The reasoning step prevents the agent from hallucinating policy details.
• Planning + Multi-Agent — document processing pipelines where one agent extracts data, another validates it, and a third routes it to the right system.
• Reflection + Memory — knowledge management agents that learn which answers get accepted and which get corrected, improving over time.

The patterns compose. The best agents I have built use 3–4 patterns together, not just one.

Check out the repo: github.com/GGCryptoh/agentic-design-patterns.