The Good Tech Companies - Inside the ReAct Design Pattern: How Modern AI Thinks and Acts

Episode Date: June 17, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/inside-the-react-design-pattern-how-modern-ai-thinks-and-acts. Let’s dig into the ReAct design pattern that’s gaining the most traction in the emerging Agentic AI world Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #agentic-ai-workflows, #react-design-pattern, #react-ai-pattern, #ai-agents-architecture, #ai-prompting-techniques, #react-vs-regular-ai, #bright-data-ai-tools, #good-company, and more. This story was written by: @brightdata. Learn more about this writer by checking @brightdata's about page, and for more stories, please visit hackernoon.com. ReAct (Reasoning + Acting) is the AI design pattern making agents smarter. It's a loop where LLMs think step-by-step, act using external tools, and observe results to refine their approach. This turns basic AI into adaptable, transparent problem-solvers for complex tasks, pushing beyond simple chatbots into true intelligent agent workflows!

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Inside the React design pattern, how modern AI thinks and acts, by bright data. Another week, another AI trend lighting up the timeline. This time, it's React, nope, not the JavaScript one you already know and love. We're talking about the reasoning plus acting pattern that's making serious noise in the world of AI agents. Originally introduced back in 2022, which is practically ancient in AI years, the React pattern is suddenly everywhere, and for good reason. Follow along as we unpack what it is, how it works, and how to implement it in your own agentic workflow. Scared of the AI wave? Wave nah, it's time to react. What's the react design pattern? You might be thinking, uh, another react article in 2025?

Haven't we talked about this for like, a decade? Is this react, but for AI now? Or maybe, sure, I know react design patterns, paper hold up. Paper we're talking about a different kind of react here, in the world of AI, react, which comes from, reasoning, plus, acting, is a design pattern where LLMs combine reasoning and acting to solve complex tasks more effectively or produce more adaptable and accurate results. Down finger lets break it down with a tasty analogy. Down finger say you're building an AI robot chef robot man cook robot. If you just say, make a sandwich, a basic AI system might ask an LLM for instructions and return a static recipe. Memo but a React powered agent?

Totally different game. First, it reasons. Wait, what kind of sandwich? Do I have the ingredients? Where's the bread? Then it acts. Opens the fridge, grabs what it needs, slices, stacks, and voila! BLT complete.

Sandwich thus, React doesn't just reply. It thinks, plans, and executes. Step by step. Footprint footprint footprint that pattern was first introduced in the 2022 paper. React. Synergizing reasoning and acting in language models. And it's blowing up in 2025 as the backbone of modern agentic AI and agentic rag-based agents.

Exploding head now, how's that possible, and how does this design pattern actually work? Let's find out, magnifying Glass React origins, how a 2022 paper sparked an AI workflow revolution. Back in late 2022, the React, Synergizing Reasoning and Acting in Language models paper built on this idea, greater than ILM's abilities for reasoning, e.g. chain of thought prompting, and acting greater than, e.g. action plan generation, have primarily been studied as separate topics. Greater than, here, we, explore the use of LLMs to generate both reasoning traces and greater than task-specific actions in an interleaved manner.

In other words, brain plus biceps equals collision. At that time, LLMs were mostly brainy assistants, generating text, answering questions, writing code. But then came the shift. By late 2022, yep, right when Chad GPT launched on November 30th, devs started wiring LLMs into real software workflows. Things got real, fast forward to today. Welcome to the age of AI agents robot detective robot, autonomous systems that reason, take action, self-correct, and get stuff done. In this new AI, Agentic, era, the React pattern, once just a neat academic idea, is now one

of the most common architectures for building goal-oriented, decision-making AI agents. Even IBM mentions React as a core building block for Agentic rag workflows. Alright, so React comes from the past, but it's shaping the future. Crystal ball now hop in the DeLorean, 88 miles per hour, baby. High voltage, we're heading back to the future to see how this pattern works in practice, and how to implement it. React applied to modern agentic AI workflows. Think of React as the MacGyver of AI.

Wrench screwdriver toolbox instead of just spitting out an answer like your typical LLM. React systems think, act, and then think again. It's not magic sparkles. Wrench screwdriver toolbox instead of just spitting out an answer like your typical LLM, React systems think, act, and then think again. It's not magic sparkles, it's when chain of thought reasoning meets real world action. Specifically, a React agent is based on a loop. 1. Reasoning. Think thinking face.

Start with a prompt like, plan a weekend trip to NYC. The agent generates thoughts. I need flights, a hotel, and a list of attractions. Quote.2 Action selection, act hammer and wrench. Based on its reasoning, the agent picks a tool, for example, via an MCP integration, say, an API to search for flights, and executes it. 3. Observation, observe magnifying glass. The tool returns data, e.g. flight options,

this is fed back to the agent, which incorporates it into the next reasoning step. Loop, repeat repeat button, the cycle continues, the agent uses new thoughts to select another tool, e.g. hotel search search gets more data, updates its reasoning, all inside a top level loop. You can picture that thinking of a, while not done, loop. At each iteration, the agent generates a new reasoning step. Selects the best tool for the task, executes the action, parses the result, checks if the goal is met.

This loop continues until a final answer or goal state is reached. How to implement React So, you want to put React into action with real world agents. Here's a common setup. The show kicks off with an orchestrator agent, think crew AI or a similar framework, driving the main React loop. This top level agent, powered by your LLM of choice, delegates the initial request to a dedicated reasoning agent.

The reasoning agent, instead of rushing, breaks down the original prompt into apprecise list of actionable steps or subtasks. It's the brain meticulously planning the strategy. Next, these tasks are handed off to an acting agent. This is where the rubber meets the road. This agent is your tool wielder, integrated directly with an MCP server, for accessing external data or tools like web scrapers or databases, or communicating with other specialized agents via 2A protocols.

It's tasked with actually performing the required actions. The results of these actions aren't ignored. They're fed to an observing agent. This agent scrutinizes the outcome, deciding if the task is complete and satisfactory, or if more steps are needed. If further action is required, the loop restarts, sending the agents back to refine the process. This continuous cycle runs until the observing agent declares the result, ready, sending that final output back up to the orchestrator agent, which then delivers it to the inquirer. As you can see, the easiest way to bring React to life is with a multi-agent setup.

Still, you can pull it off with a single, simple, mini-agent, too. Just check out the example in the video below. youtube.com. watch. v equals p m h p b q m n j g and embeddable equals true react vs. regular ai workflows aspect. regular ai workflow react powered ai workflow core process direct generation. single inference pass iterative. reasoning plus acting. loop. step by step thinking and execution external interaction may be limited to no external tool use actively leverages tools adaptability less adaptable relies on training data highly adaptable refined strategy based on real-time feedback problem solving best for straightforward single turn tasks tasks. Excel's at complex, multi-step problems requiring external info and dynamic solutions feedback loop generally no explicit feedback for self-correction explicit real-time feedback loop

to refine reasoning and adjust actions transparency often a black box hard to trace logic. High visibility. Explicit chain of thought and sequential actions show reasoning and output at each step use case fits simple Q&A. Content generation complex tasks. Trip planning, research, multi-tool workflows implementation simple. Requires AI chat integrations complex. Requires loop logic, tool integration, and might involve a multi-agent architecture pros and cons thumbs up super accurate and adaptable. Thinks, acts, learns, and course corrects on the fly.

Thumbs up handles gnarly problems. Excels at complex, multi-step tasks requiring external info thumbs up external tool power. Integrates with useful tools and external data sources. Thumbs up transparent and debuggable. See every thought and action, making debugging a breeze. Thumbs down increased complexity. More moving parts means more to design and manage. Thumbs down higher latency and calls. Iterative loops, external calls, and orchestration overhead can make the overall fees higher and responses slower.

That's the costo pay for more power and accuracy. What you need to master React, let's be real, without the right tools, a React agent isn't much more powerful than any other run of the mill AI workflow. Tools are what turn reasoning into action. Without them, agents are just thinking really hard. At Bright Data, we've seen the pain of connecting AI agents to meaningful tools.

So we've built an entire infrastructure to fix that. No matter how you design your agents, we've got them covered. Datapacks. Curated, real-time, AI-ready datasets perfect for RAG workflows. Package. MCP servers. AI-ready servers loaded with tools for data parsing, browser control, format conversion, and more. Gear, search APIs. Search APIs your LLMs can tap into for fresh, accurate web results, built for RAG pipelines. Magnifying Glass, agent browsers, AI-controllable browsers that can scrape the web, dodge IP bans, solve CAPTCHAs, and keep going. SpiderWeb, and this tool stack is constantly expanding. Upward trend right arrow take a look at what bright data's eye and buy infrastructure can unlock for your next-gen agents.

Extra, the React cheat sheet. Before wrapping up, take a moment to clear the air. There's a lot of buzz, and confusion, around the term, React, especially since multiple teams are using it in different contexts. So, here's a no-fluff glossary to help you keep it all straight. React design pattern, an AI pattern that merges reasoning and acting. An agent first thinks like chain of thought reasoning, then acts like doing a web search

and finally gives a refined answer. React prompting, a prompt engineering technique that nudges LLMs to show their reasoning process step by step and take actions mid thought. It's designed to make responses more accurate, transparent, and less hallucination prone. Learn more about React Prompting. React-agentic pattern, just another name for saying, React design pattern. React agent, any AI agent that follows the React loop. It reasons about the task, performs actions based on that reasoning, like calling a tool, and returns the answer.

React Agent Framework, the architecture, or library, you should use to build React-style agents. It helps you implement the whole, reason-act-answer, logic in your custom AI systems. Final thoughts. Now you've got the gist of what React means in the realm of AI. Especially when IT comes to AI agents. You've seen where this design pattern came from, what it brings to the table, and how to actually implement it to power up your agentic workflows.

As we explored, bringing these next-gen workflows to life becomes easier when you have the right AI infrastructure and toolchain to back your agents up. At Bright Data, our mission is simple. Make AI more usable, more powerful, and more accessible to everyone, everywhere. Until next time, stay curious, stabled, and keep building the future of AI. Surfing Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.