As AI agents evolve beyond simple chatbots, they’re becoming more capable, adaptable, and intelligent. New design patterns have emerged to help them think, act, and collaborate in real-world settings. Here are five popular agentic AI design patterns that every AI engineer should know:
1. ReAct Agent: The Thinking, Acting, and Adjusting AI
A ReAct agent is an AI built on the “reasoning and acting” (ReAct) framework. It doesn’t follow fixed rules; instead, it thinks through problems, takes actions like searching or running code, observes the results, and decides what to do next. Just like how we humans solve problems, ReAct agents alternate between thoughts, actions, and observations to handle complex tasks and make better decisions.
2. CodeAct Agent: The AI That Writes, Runs, and Refines Code
A CodeAct Agent is an AI system designed to write, run, and refine code based on natural language instructions. It can generate code from your input, execute it in a safe environment, review the results, and improve its response based on what it learns. This allows it to solve complex, multi-step problems efficiently.
3. Self-Reflection: The AI That Learns from Its Mistakes
A Reflection Agent is an AI that can evaluate its own work, identify mistakes, and improve through trial and error—just like humans. It operates in a cyclical process: generate output, reflect on it, refine it, and repeat until the result reaches a high-quality standard. This makes it more reliable and adaptable than agents that generate content in a single pass.
4. Multi-Agent Workflow: The AI Dream Team
A Multi-Agent System uses a team of specialized agents instead of relying on a single agent to handle everything. Each agent focuses on a specific task, making them more likely to succeed and allowing for tailored prompts and independent improvement. By dividing complex problems into smaller units, multi-agent designs make large workflows more efficient, flexible, and reliable.
5. Agentic RAG: The AI That Actively Searches for Relevant Data
Agentic RAG agents take information retrieval a step further by actively searching for relevant data, evaluating it, generating well-informed responses, and remembering what they’ve learned for future use. Unlike traditional RAG, Agentic RAG employs autonomous agents to dynamically manage and improve both retrieval and generation, delivering smarter, more contextual answers.
These five design patterns are shaping the future of AI, making agents more capable, adaptable, and intelligent. As an AI engineer, knowing and understanding these patterns will help you build smarter, more autonomous systems. So, which pattern will you try first?
