The ReAct Loop: How Agents Actually Think

If you want to understand how AI agents work under the hood, you need to understand ReAct.

ReAct (Reason + Act) is a pattern where the model alternates between two types of output:

1. Thought — the model reasons in natural language about what it knows and what it needs to do next
2. Action — the model calls a tool (search, calculator, API, etc.) and observes the result

Then it loops — using the observation to inform the next thought, which leads to the next action, until it has enough information to give a final answer.

A Concrete Example

Thought: The user asked for the population of Dubai in 2024. I should search for this.
Action: search("Dubai population 2024")
Observation: Dubai's population reached approximately 3.6 million in 2024...

Thought: I have the answer. No more tools needed.
Final Answer: Dubai's population in 2024 was approximately 3.6 million.

Why It Works

The key insight is that reasoning in natural language before acting is enormously powerful. The model can plan, backtrack, and course-correct mid-task in a way that pure input→output pipelines can’t.

Where It Breaks

• Loop termination — without a good stopping criterion, agents loop endlessly
• Tool hallucination — models sometimes call tools that don’t exist or pass malformed arguments
• Context overflow — long chains of observations can exhaust the context window

My Research Scout agent hit all three of these. Each fix taught me more than any paper I’ve read.