✉️ Meet the 6 Agent Design Patterns Powering the Future of AI (Faceless & Smart)

🤖 Agents Are the Next Phase of AI — And They're Already Here

First it was prompts.
Then it was workflows.
Now… it’s agents — systems that can think, reason, reflect, and even collaborate.

You’ve seen ChatGPT generate text.
But what happens when AI starts making decisions, calling tools, and working like a teammate?

That’s what agent design patterns are about.

This issue gives you a quick tour of the 6 major agent types you’re going to see everywhere soon —
and yes, we’ll build each one together in upcoming issues.

⚙️ 1. ReAct Agent — Reason + Act

This is the classic agent model:

• It thinks (using LLM)

• It acts (using tools like search or calculator)

• Then it loops back to think again

💡 Used in most agentic frameworks like AutoGPT and LangChain.

💻 2. CodeAct Agent — LLM That Executes Code

Instead of outputting JSON or plain text, this agent can directly write and run Python code.
Great for automation, scripting, data crunching.

⚙️ Built by Manus AI, it closes the gap between thought and execution.

🧰 3. Modern Tool Use Agent — Plug-and-Play Tools

This agent connects to external APIs like Kagi Search or AWS and uses modular calls via something like MCP (Modular Command Protocol).

It's smart, scalable, and barely needs code.

🪞 4. Self-Reflection Agent — Thinks Before It Ships

Before it gives you an answer, this agent evaluates its own output, critiques it, and improves it.

⚡ Inspired by Reflexion, it turns AI into a more thoughtful assistant — not just a fast typer.

🧠 5. Multi-Agent Workflow — The Team Player Model

Multiple agents, each with a role:

• One fetches data

• One analyzes

• One summarizes

• One aggregates

Used by Gemini Deep Research, it’s about collaboration at scale.

🔄 6. Agentic RAG — Personal Search + Answer Engine

Combines RAG (Retrieval-Augmented Generation) with agent behavior:

• Pulls relevant documents from vector DBs

• Evaluates them

• Generates grounded responses

Used by tools like Perplexity AI.

🔍 TL;DR: Agent Types at a Glance

📩 What’s Coming Next

Over the next few issues, we’ll break down how to build each of these agents —
with tools like:

• ChatGPT Custom GPTs

• Flowise

• LangChain (light)

• Zapier + Google Sheets

• And zero heavy backend setup

✨ Final Thought

LLMs were the start.
Agents are the system.

And if you know how they work — you’ll never build alone again.

👋 Next up in The Artificial Newsletter:
“How I Built a ReAct Agent Using ChatGPT + Browser Tools — No Python, No Problem”