ReAct Agents: What They Are & How to Build Your Own!

Earlier, checking the weather meant opening a browser, searching for a forecast, and reading through charts.

Some people used weather apps, typing their location and scrolling through updates to decide if they needed an umbrella.

This took time and effort because they had to search, read, and make decisions on their own.

Now, ReAct agents make this process simple. Instead of checking weather updates manually, we can just ask, and get a clear answer based on real-time data.

For example:

🗣️ Me: "Hey, should I take my umbrella?"
🤖 Agent: "It looks like it will rain between 4-6 PM, which is when you’ll be returning. You should take your umbrella."

The AI does not just read out the forecast. It thinks about the request, checks live weather updates and considers the user’s schedule before answering.

Unlike traditional assistants that only give basic weather reports, ReAct Agents combine reasoning, real-time data, and decision-making to provide accurate and useful responses.

This small change—from asking "What's the weather?" to "Do I need an umbrella?", shows how AI is becoming smarter and more helpful.

Instead of giving raw data, it tells you exactly what you need to know, making AI faster, easier, and more practical in everyday life.

What Are ReAct Agents?

ReAct agents combine reasoning (step-by-step thinking) with action (using tools or APIs). Unlike traditional AI models that rely only on pre-trained knowledge, ReAct agents think before they act, just like humans.

Why Do ReAct Agents Matter?

They reduce hallucinations by verifying facts before responding.
They improve decision-making by using real-time data instead of guessing.

Traditional LLMs vs. ReAct Agents

Traditional LLMs:

Only use past training data.
Cannot check the current information.
Example: If you ask, "Should I take an umbrella in San Francisco today?", an LLM might say:
"It often rains in SF. Bring an umbrella."

ReAct Agents:

Think before responding.
Use live data from APIs.
Example: A ReAct agent follows these steps:
1. Think: "Check the weather forecast for San Francisco this evening."
2. Act: Calls a weather API.
3. Observe: Sees "0% rain at 7 PM."
4. Answer: "No rain is predicted. You don’t need an umbrella."

Core Concepts: How ReAct Works?

ReAct agents follow a three-step process to think, take action, and refine their responses based on real-world data.

Three-Step Workflow (Google Research):

1. Think: The agent plans its next steps based on the question.

Example: "Check the evening weather forecast for San Francisco."

1. Act: It executes the plan by using tools or APIs.

Example: Calls a weather API to get real-time data.

3. Observe: The agent analyzes the result and decides what to do next.

Example: If the API shows "0% chance of rain at 7 PM," the agent updates its response:
"No rain expected. You don’t need an umbrella."

Key Components of ReAct Agents

Tools: External sources like weather APIs, search engines, and databases that provide real-time information.
- Example: A ReAct agent can use Google Search to verify breaking news.
Memory: The agent remembers previous actions and results to improve future decisions.
- Example: If the agent checked the weather earlier in the day, it won’t ask the same API again unless necessary.

ReAct vs. Other Agent Frameworks

ReAct agents stand out from other AI agent frameworks because they think before acting. Unlike simple function-calling agents, ReAct agents reason through a problem before deciding what actions to take. This makes them more adaptable and reliable.

ReAct vs. Function-Calling Agents

ReAct Agents:
- Follow a dynamic loop of thinking, acting, and observing.
- Can solve complex problems step by step.
- Adapt their approach based on real-time results.
Function-Calling Agents:
- Work on predefined rules and conditions.
- Execute one-step actions without analyzing the problem deeply.
- Example: If temperature < 60°F, suggest wearing a coat.

Example: Checking the Weather

Function-Calling Agent:
- Receives a query: "Should I take an umbrella?"
- Calls a weather API and returns: "10% chance of rain."
- User has to interpret the result.
ReAct Agent:
- Thinks: "The user is asking if they need an umbrella, not just the rain percentage."
- Acts: Calls the weather API.
- Observes: "10% chance of rain between 4-6 PM, which matches the user's return time."
- Answers: "It might rain when you're returning. You should take an umbrella."

Strengths of ReAct Agents

Handles Uncertainty:
- Function-calling agents fail when information is incomplete or unclear.
- ReAct agents break down the problem, check multiple sources, and refine their answers.
- Example: If a drizzle is predicted, a function-calling agent may not suggest an umbrella, while a ReAct agent considers "light rain + wind = wet clothes" and advises accordingly.
Adapts to Changing Information:
- Function-calling agents can’t adjust if new data arrives.
- ReAct agents re-evaluate and update their response.
- Example: A ReAct agent tracking a stock price can adjust its decision if a new market report appears.

Use Cases for ReAct Agents

ReAct agents have many practical applications because they can combine reasoning with actions.

1. Healthcare:

AI doctors can analyze symptoms, check medical records, and suggest treatments in real time.
Example: A patient says, "I have a headache and fever."
- Function-Calling Agent: "Here are common causes of fever."
- ReAct Agent: "Check if symptoms match flu. If yes, suggest rest and hydration. If no, recommend a doctor visit."

2. Customer Support:

AI assistants can understand complaints, check databases, and suggest solutions without needing human intervention.
Example: A user asks, "Where is my package?"
- Function-Calling Agent: "Tracking ID: 123456."
- ReAct Agent: "Check tracking → See package delayed → Offer refund or apology."

3. Finance:

AI trading bots can analyze stock trends, predict market shifts, and execute trades based on live data.
Example: A trader asks, "Should I buy Tesla stock today?"
- Function-Calling Agent: "Current price: $800."
- ReAct Agent:
  - "Check stock history → Analyze news sentiment → Compare market trends → Suggest a buy/sell decision."

ReAct agents go beyond simple automation. They think, act, and improve, making AI more intelligent, flexible, and reliable for real-world tasks.

Let's Try It Out

I continuously need to write high-quality blogs, and manually creating content every week takes time and effort. I wanted an automated solution that could help me generate high-quality posts while keeping me in control of the final review.

So, I built a ReAct-powered AI agent using Crew AI to streamline my content creation workflow.

How My AI Content Agent Works

Thinks: The agent decides on a relevant content topic for the week by analyzing trends, past content, and audience engagement.
Acts: It generates a complete post-draft based on the selected topic.
Observes & Improves: It creates a draft on Medium for me to review, ensuring that the final content aligns with my voice and intent.

This system automates brainstorming, writing, and formatting, so all I need to do is review and hit publish. It not only saves time but also keeps my content consistent and high-quality.

By integrating ReAct Agents into content workflows, we can automate complex tasks that require both reasoning and action, proving how useful they are in real-world applications beyond technical use cases.

You can try the code here.

Challenges & Best Practices

ReAct agents improve AI decision-making, but they also face challenges. Here’s how to handle them effectively:

Tool Reliability: APIs can fail or return incorrect data. A ReAct agent should detect failures and try backup sources instead of stopping.
Prompt Design: Clear prompts help the agent think properly. Avoid overly complex instructions that might confuse the model.
Cost Optimization: ReAct agents rely on external tools like APIs, which can be expensive. Optimize by limiting unnecessary API calls during testing.

Future of ReAct Agents

ReAct agents will continue evolving to handle more complex tasks:

Multimodal Extensions: Future ReAct agents will process not just text but also images and audio. For example, an agent could look at sky photos and decide if you need an umbrella instead of just relying on weather APIs.
Self-Improvement: Agents will learn from mistakes and adjust over time. If an agent misses hail warnings, it will update its rules to avoid the same mistake again.

Conclusion

ReAct agents improve AI by combining reasoning and action. They help AI think before acting, reducing mistakes and improving decisions. By using real-time data, they adapt to different situations and solve real-world problems efficiently.

FAQs

1. What are ReAct Agents in AI?

ReAct Agents are AI models that combine reasoning and acting, allowing them to analyze data, make decisions, and take actions dynamically in real-time.

2. How do ReAct Agents differ from traditional AI models?

Unlike traditional AI, which separates reasoning from action, ReAct Agents integrate both, enabling real-time learning, decision-making, and adaptability in complex scenarios.

3. What are the key applications of ReAct Agents?

ReAct Agents are used in autonomous robotics, virtual assistants, AI-driven customer support, financial forecasting, and smart automation, improving efficiency across industries.

ReAct Agents: What They Are & How to Build Your Own from Scratch!