AI Agents & ReAct: How AI Learns to Take Action
By Learnia Team
AI Agents & ReAct: How AI Learns to Take Action
This article is written in English. Our training modules are available in French.
What if an AI could do more than just answer questions? What if it could reason about a problem, decide what action to take, observe the result, and keep going until the task is done?
Welcome to the world of AI Agents.
What Is an AI Agent?
An AI agent is a system where an LLM can:
- →Reason about what to do
- →Take actions using external tools
- →Observe the results
- →Iterate until the goal is achieved
It's the difference between asking "What's the weather?" and asking "Plan my weekend trip based on weather and my calendar."
From Chat to Action
Traditional LLM (Reactive)
User: What's 2 + 2?
AI: 4
The AI answers and waits.
AI Agent (Agentic)
User: Research competitors and create a summary report
AI: [Thinks] I need to search for competitors first
[Action] Search web for "competitors in X industry"
[Observe] Found 5 companies...
[Thinks] Now I should get details on each
[Action] Fetch info on Company A...
...
[Final] Here's your competitor summary report
The AI takes multiple steps autonomously to complete a complex task.
The ReAct Framework
ReAct stands for Reasoning + Acting. It's a powerful pattern for building agents.
The ReAct Loop
- →Thought: The AI reasons about what to do next
- →Action: The AI chooses a tool and uses it
- →Observation: The AI receives the result
- →Repeat until the task is complete
Example ReAct Trace
Question: What is the population of the capital of France?
Thought: I need to find the capital of France first.
Action: Search[capital of France]
Observation: Paris is the capital of France.
Thought: Now I need the population of Paris.
Action: Search[population of Paris]
Observation: Paris has a population of 2.1 million.
Thought: I have the answer.
Final Answer: The population of Paris, capital of France, is 2.1 million.
Why ReAct Works
1. Explicit Reasoning
The "Thought" step forces the AI to plan, reducing impulsive or incorrect actions.
2. Grounded in Real Data
Actions fetch real information, so answers are based on facts, not just training data.
3. Transparent Process
You can see the AI's reasoning, making it easier to debug and trust.
4. Flexible and Composable
Different tools can be plugged in: search, calculators, APIs, databases, and more.
Common Tools for Agents
Agents are only as useful as their tools. Common ones include:
| Tool | What It Does | |------|--------------| | Web Search | Find current information online | | Calculator | Perform accurate math | | Code Interpreter | Execute Python code | | Database Query | Fetch data from databases | | API Calls | Interact with external services | | File Operations | Read/write documents |
Agents in the Real World
AI agents are powering:
- →Research assistants that gather and synthesize information
- →Customer support that checks orders, processes refunds, updates accounts
- →Coding assistants that run tests, debug, and fix code
- →Personal assistants that book appointments and manage tasks
- →Data analysts that query databases and create reports
Challenges with Agents
Agents are powerful but not perfect:
- →Error propagation: One bad step can derail the whole process
- →Cost: Multiple LLM calls add up quickly
- →Latency: Multi-step processes take time
- →Safety: Agents with real-world actions need careful guardrails
- →Reliability: Current agents can get stuck or loop
Key Takeaways
- →AI Agents combine reasoning with real-world action
- →ReAct = Reasoning + Acting in an iterative loop
- →Agents use tools to interact with external systems
- →The Thought → Action → Observation cycle enables complex tasks
- →Agents are transforming what AI can accomplish autonomously
Ready to Build Your Own AI Agents?
This article covered the what and why of AI agents and ReAct. But building reliable, production-ready agents requires deeper knowledge.
In our Module 6 — AI Agents & ReAct, you'll learn:
- →Designing effective agent architectures
- →Implementing the ReAct pattern from scratch
- →Building and integrating custom tools
- →Handling errors and preventing infinite loops
- →Safety patterns for agents with real-world actions
Module 6 — AI Agents & ReAct
Create autonomous agents that reason and take actions.