5 Agentic AI Design Patterns That Power Smarter Autonomous Systems

As AI agents move beyond simple chat interfaces, engineers are adopting distinct agentic design patterns that shape how agents think, act, and collaborate. These patterns help agents reason through tasks, operate external tools, write and run code, self-correct, and coordinate with other agents to solve real-world problems.

ReAct Agent

A ReAct agent follows the “reasoning and acting” approach, alternating between step-by-step thought and concrete actions such as searching, querying tools, or running code. Rather than producing a single static output, a ReAct agent thinks about the problem, performs an action, observes results, and then updates its reasoning. This loop — thought, action, observation — mirrors how humans tackle complex tasks and enables the agent to adapt mid-process.

ReAct architectures typically give the agent access to a set of tools and let it decide when to invoke them. Conditional pathways let the agent skip tools when unnecessary and call them when observations demand new information or capabilities.

CodeAct Agent

A CodeAct Agent is built to generate, execute, and refine code in response to natural language instructions. It goes beyond text generation by executing code in a controlled environment, analyzing the results, and iterating until the desired outcome is reached.

Key components include a code execution environment, workflow orchestration, prompt engineering, and memory for storing context. For example, Manus AI uses a structured agent loop: it parses a user request, selects tools or APIs, runs commands in a secure Linux sandbox, inspects the execution output, and iterates until the task is complete. This approach is especially powerful for multi-step programming tasks, data analysis, and automated testing.

Self-Reflection

Reflection Agents evaluate their own outputs to detect mistakes and improve them via iterative refinement. The agent generates an initial response — such as text or code — then inspects that output, identifies errors or weak points, and revises accordingly. This cyclical self-review can repeat several times to raise quality and reliability.

Self-reflection is particularly useful for tasks that benefit from careful verification, nuanced reasoning, or expert-like adjustments. Agents that reflect tend to be more robust than single-pass generators because they can catch and fix issues autonomously.

Multi-Agent Workflow

Multi-agent systems decompose a problem into specialized roles handled by multiple agents working in parallel or sequence. Instead of a single generalist agent trying to manage everything, each agent focuses on a specific responsibility — for example, research, coding, and review — which improves precision and throughput.

This pattern offers several benefits: prompts and instructions can be tailored to individual agents, specialized or fine-tuned models can be used where appropriate, and components can be evaluated and updated independently. By splitting workflows into smaller tasks, multi-agent designs make complex projects more scalable and maintainable.

Agentic RAG

Agentic RAG (retrieval-augmented generation) extends traditional RAG by using autonomous agents to manage retrieval, evaluation, generation, and memory. Instead of a static pipeline that retrieves documents and then generates a response, Agentic RAG actively searches for relevant data, assesses its value, synthesizes answers, and stores useful findings for future use.

Architecturally, an Agentic RAG system usually includes a retrieval layer (indexing and query processing using techniques like BM25 or dense embeddings), a generation model that converts retrieved content into contextual responses, and an agent layer that coordinates these steps and maintains memory. The result is a more dynamic, context-aware retrieval and generation process that produces richer, more accurate answers than traditional RAG setups.

These five patterns — ReAct, CodeAct, Self-Reflection, Multi-Agent workflows, and Agentic RAG — each offer a distinct strategy for building more capable, adaptable AI agents. Engineers can mix and match these approaches depending on task complexity, required autonomy, and safety constraints.