A ReAct (Reason + Act + Observe) loop is the core mechanism behind most AI agents. Unlike parallel tool calling, which assumes all needed tools and arguments are known upfront, a ReAct loop lets a model iteratively call tools, observe results, and decide what to do next based on what it just learned. Using a practical example of a weather-dependent currency bet, the post walks through a Python implementation with OpenAI's API, showing how the loop enables conditional branching — skipping unnecessary tool calls when earlier results make them irrelevant — and why this matters for both correctness and cost.
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But what exactly is a ReAct loop?Same Tools, New TrickWatching the loop thinkOn my mind189 Impressions