5 Most Popular Agentic AI Design Patterns Every AI Engineer Should Know

As AI agents evolve beyond simple chatbots, new design patterns have emerged to make them more capable, adaptable, and intelligent. These agentic design patterns determine how agents think, act, and collaborate to solve complex problems in real-world environments. Whether it’s thinking through tasks, writing and executing code, connecting to external tools, or even thinking about their own outputs, each style represents a distinct approach to building smarter, more autonomous systems. Here are five of the most common interaction design patterns that every AI engineer should know.

Interaction agent

ReAct Agent is an AI agent built on “Logic and action“The ReAct framework, which combines step-by-step thinking with the ability to use external tools. Instead of following fixed rules, one thinks about problems, takes actions such as research or running code, observes the results, and then decides what to do next.”

The ReAct framework works in a way that is very similar to the way humans solve problems – by thinking, acting, and adapting along the way. For example, imagine you’re planning dinner: You start thinking, “What do I have at home?” (inference), then check your refrigerator (verb). When you see only vegetables (observation), you can modify your plan – “I will make pasta with vegetables.” In the same way, ReAct agents alternate between thoughts, actions, and feedback to handle complex tasks and make better decisions.

The image below shows the basic structure of the ReAct agent. The agent has access to different tools that he can use when needed. It can think independently, decide whether to invoke an instrument, and replay procedures after making adjustments based on new feedback. Dotted lines represent conditional paths, showing that an agent may choose to use a tool node only when it deems it necessary.

CodeAct Agent

A CodeAct Agent is an artificial intelligence system designed to write, run, and optimize code based on natural language instructions. Instead of just creating a script, he can actually execute the code, analyze the results, and fine-tune his approach – allowing him to solve complex, multi-step problems efficiently.

At its core, CodeAct allows an AI Assistant to:

• Generate code from natural language input
• Execute this code in a safe and controlled environment
• Review implementation results
• Improve his response based on what he learns

The framework includes key components such as a code execution environment, workflow definition, agile architecture, and memory management, all of which work together to ensure the agent can reliably perform real-world tasks.

A good example of this is Manus AI, which uses a structured agent loop to process tasks step by step. It first analyzes the user request, chooses the appropriate tools or application programming interfaces (APIs), executes the commands in a secure Linux sandbox, and then iterates based on feedback until the job is finished. Finally, it sends the results to the user and enters standby mode, waiting for the next instruction.

Self-reflection

A reflection agent is an artificial intelligence that can step back and evaluate its work, identify errors, and improve through trial and error — similar to the way humans learn from feedback.

This type of agent works in a cyclic process: it first generates raw output, such as text or code, based on the user’s prompt. It then reflects this output, discovering errors, inconsistencies, or areas for improvement, often applying expert-like reasoning. Finally, it improves the output by incorporating its own feedback, repeating this cycle until the result reaches a high quality level.

Reflection agents are particularly useful for tasks that benefit from self-evaluation and iterative improvement, making them more reliable and adaptable than agents that generate content in a single pass.

Multi-agent workflow

A multi-agent system uses a team of specialized agents rather than relying on one agent to handle everything. Each agent focuses on a specific task and leverages its strengths to achieve better overall results.

This approach offers several advantages: focused agents are more likely to succeed at their specific tasks than a single agent managing many tools; Separate prompts and instructions can be designed for each agent, even allowing for the use of finely tuned LLMs; Each factor can be evaluated and improved independently without impacting the broader system. By breaking down complex problems into smaller, manageable units, multi-agent designs make large workflows more efficient, flexible, and reliable.

The image above depicts a multi-agent system (MAS), showing how a single user router is broken down into specialized tasks that are handled in parallel by three distinct agents (searching, coding, and reviewing) before being compiled into a final, high-quality output.

RAG Agent

RAG agents take information retrieval one step further by actively searching for and evaluating relevant data, creating informed responses, and remembering what they have learned for future use. Unlike traditional Native RAG, which relies on static retrieval and generation processes, Agentic RAG uses independent agents to dynamically manage and optimize both retrieval and generation.

The architecture consists of three main components.

• The retrieval system fetches relevant information from the knowledge base using techniques such as indexing, query processing, algorithms such as BM25 or dense embeddings.
• The generation model, typically a fine-tuned LLM, transforms the retrieved data into contextual embeddings, focuses on essential information using mechanisms of interest, and generates coherent and fluent responses.
• The agent layer coordinates the retrieval and generation steps, making the process dynamic and context-aware while enabling the agent to remember and make use of previous information.

Together, these components allow Agentic RAG to deliver smarter, more contextual answers than traditional RAG systems.

I am a graduate of Civil Engineering (2022) from Jamia Millia Islamia University, New Delhi, and I have a keen interest in data science, especially neural networks and their applications in various fields.