The shift from chatbots to agents is not about bigger models but about architecture. ReAct (Reasoning + Acting) is a pattern where an AI system alternates between thinking about a goal and taking actions, looping until completion. This post explains the core loop: the model reasons about the next step, calls a tool or retrieves information, observes the result, and repeats. Unlike a one-shot ChatGPT response, this loop allows the system to handle multi-step tasks like booking flights or running code. For developers, understanding ReAct is foundational for building reliable agents. Key takeaways include the importance of structured reasoning traces, tool integration, and stopping conditions. The pattern is simple but powerful, and it underpins many modern agent frameworks. This explainer is a good starting point for teams designing their first agentic workflows.
A practical breakdown of the ReAct agent paradigm, showing how iterative reasoning-action loops enable goal-driven AI systems beyond simple chat.