AI agents can now tackle multi-step problems thanks to the ReAct loop, a pattern combining reasoning and action. Unlike single tool calls, this iterative process allows models to assess missing information, execute a tool, observe the result, and then re-evaluate. This mechanism enables agents to handle tasks where outcomes depend on previous steps, moving beyond simple, independent queries.

This ReAct loop leverages existing tool-calling capabilities but applies them iteratively. For instance, determining the USD equivalent of a bet won in EUR requires first checking if it rained in Athens. The agent must call a weather tool, observe the precipitation, and then decide if a currency conversion tool is necessary. This dependency chain is precisely what ReAct addresses.

The loop consists of three steps: Reason, Act, and Observe. The AI reasons about its knowledge and information gaps. It then Acts by calling a tool to gather needed data. Finally, it Observes the tool's output, incorporating it into its context. This cycle repeats until the AI has sufficient information to answer the user's query directly.

This approach avoids the need for new tools, instead applying existing ones like weather and currency conversion in a more dynamic sequence. The ReAct loop allows AI agents to adapt their plans based on newly acquired information, making them more versatile problem-solvers.