The 5 Most Popular Agent 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 agent design patterns define how agents think, act, and collaborate to solve complex problems in real-world environments. Whether reasoning through tasks, writing and executing code, connecting to external tools, or even reflecting on your own output, each pattern represents a unique approach to building smarter, more autonomous systems. Here are five of the most popular agent design patterns that every AI engineer should know.

Reactant

The ReAct agent is aReasoning and Action“The (ReAct) framework, which combines step-by-step thinking with the ability to use external tools. Rather than following fixed rules, it thinks about the problem, takes actions like searching or running code, observes the results, and then decides what to do next.

The ReAct framework works much like how humans solve problems—by thinking, acting, and adapting along the way. For example, imagine planning dinner: You first think, “What do I have at home?” (reasoning), then check your refrigerator (action). Seeing only vegetables (observation), you adjust your plan – “I’ll make pasta with vegetables.” Likewise, the ReAct agent alternates between thoughts, actions, and observations to handle complex tasks and make better decisions.

The following figure shows the basic architecture of ReAct Agent. Agents have access to a variety of tools that can be used when needed. It can reason independently, decide whether to invoke a tool, and rerun operations with adjustments based on new observations. The dashed line represents a conditional path—indicating that the agent can only choose to use a tool node if it deems it necessary.

Code Act Agent

CodeAct Agent is an artificial intelligence system designed to write, run and optimize code based on natural language instructions. Not only can it generate text, it can actually execute the code, analyze the results, and adapt its approach, allowing it to efficiently solve complex multi-step problems.

At its core, CodeAct enables AI assistants to:

• Generate code from natural language input
• Execute this code in a safe, controlled environment
• View execution results
• Improve its response based on what it learns

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

Manus AI is a great example of this, using a structured agent loop to process tasks step-by-step. It first analyzes the user’s request, selects the right tool or API, executes the command in a secure Linux sandbox, and iterates based on feedback until the job is done. Finally, the results are submitted to the user and enter standby mode, waiting for the next instruction.

self-reflection

A reflective agent is a type of artificial intelligence that can step back and evaluate its own work, identify errors, and improve through trial and error—similar to how humans learn from feedback.

This type of agent operates in a cyclic process: it first generates initial output, such as text or code, based on the user’s prompts. Next, it reflects on the output, identifying errors, inconsistencies, or areas for improvement, often applying expert-like reasoning. Finally, it refines the output by incorporating its own feedback, repeating the cycle until the results meet high quality standards.

Reflective 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 once.

Multi-agent workflow

Multi-agent systems use a dedicated set of agents rather than relying on a single agent to handle everything. Each agent focuses on a specific task, leveraging its strengths to achieve better overall results.

This approach has several advantages: a dedicated agent is more likely to successfully complete its specific task than a single agent managing many tools; individual tips and instructions can be tailored to each agent, even allowing for the use of fine-tuned LLMs; and each agent can be evaluated and improved independently without impacting the broader system. By dividing complex problems into smaller, manageable units, multi-agent design makes large workflows more efficient, flexible, and reliable.

The above diagram visualizes a multi-agent system (MAS), illustrating how a single user prompt is broken down into specialized tasks that are processed in parallel by three different agents (research, coding, and reviewers) and then synthesized into a final high-quality output.

AgentRAG

Agent RAG Agents take information retrieval one step further by actively searching for relevant data, evaluating it, generating well-informed responses, and remembering what they learned for future use. Unlike traditional Native RAG, which relies on static retrieval and generation processes, Agentic RAG uses autonomous agents to dynamically manage and improve retrieval and generation.

The architecture consists of three main components.

• Retrieval systems use techniques such as indexing, query processing, and algorithms such as BM25 or dense embedding to obtain relevant information from the knowledge base.
• Generative models are typically fine-tuned LLMs that convert retrieved data into contextual embeddings, use attention mechanisms to focus on key information, and generate coherent, smooth responses.
• The agent layer coordinates the retrieval and generation steps, making the process dynamic and context-aware while enabling the agent to remember and leverage past information.

Together, these components enable Agentic RAG to provide smarter, more contextual answers than traditional RAG systems.

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