Inside the ReAct Design Pattern: How Modern AI Thinks and Acts | HackerNoon

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 + 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? 🌊 Nah. It’s time to Re-Act!

What’s the ReAct Design Pattern?

You might be thinking, ”Ugh… 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!”

✋ Hold up! ✋ We’re talking about a different kind of ReAct here!

In the world of AI, ReAct—which comes from “Reasoning” + “Acting”—is a design pattern where LLMs combine reasoning and acting to solve complex tasks more effectively or produce more adaptable and accurate results.

👇 Let’s break it down with a tasty analogy! 👇

Say you’re building an AI robot chef 🤖 👨‍🍳 🤖. If you just say “make a sandwich,” a basic AI system might ask an LLM for instructions and return a static recipe. 📝

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 voilà—BLT complete! 🥪

Thus, ReAct doesn’t just reply. It thinks, plans, and executes. Step. By. Step. 👣 👣 👣

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. 🤯

Now, how’s that possible, and how does this design pattern actually work? Let’s find out! 🔍

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:

“[LLMs’] abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. [Here, we] explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner…”

In other words: 🧠 + 💪 = 💥.

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 ChatGPT launched on Nov 30), devs started wiring LLMs into real software workflows. Things got real.

Fast-forward to today: welcome to the age of AI agents 🤖 🕵 🤖—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. 🔮

Now hop in the DeLorean (88 MPH, baby! ⚡)—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. 🔧 🪛 🧰

Instead of just spitting out an answer like your typical LLM, ReAct systems think, act, and then think again. It’s not magic ✨—it’s when chain-of-thought reasoning meets real-world action.

Specifically, a ReAct agent is based on a Think 🤔 → Act 🛠️ → Observe 🔍 → Repeat 🔁 loop:

1. Reasoning (Think 🤔): 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.”
2. Action selection (Act 🛠️): 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 🔍): 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 🔁): The cycle continues. The agent uses new thoughts to select another tool (e.g., hotel 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 CrewAI 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 a precise list of actionable steps or sub-tasks. 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 A2A 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 Reasoning -> Acting -> Observing 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:

ReAct vs “Regular” AI Workflows

Pros and Cons

👍 Super accurate and adaptable: Thinks, acts, learns, and course-corrects on the fly. 👍 Handles gnarly problems: Excels at complex, multi-step tasks requiring external info 👍 External tool power: Integrates with useful tools and external data sources. 👍 Transparent and debuggable: See every thought and action, making debugging a breeze.

👎 Increased complexity: More moving parts means more to design and manage. 👎 Higher latency and calls: Iterative loops, external calls, and orchestration overhead can make the overall fees higher and responses slower (that’s the cost to 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:

• Data packs: Curated, real-time, AI-ready datasets perfect for RAG workflows. 📦
• MCP servers: AI-ready servers loaded with tools for data parsing, browser control, format conversion, and more. ⚙️
• SERP APIs: Search APIs your LLMs can tap into for fresh, accurate web results — built for RAG pipelines. 🔎
• Agent browsers: AI-controllable browsers that can scrape the web, dodge IP bans, solve CAPTCHAs, and keep going. 🕸️

…And this toolstack is constantly expanding. 📈

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, stay bold, and keep building the future of AI. 🏄