ReAct agents explained: concepts & practical uses

ReAct agents use an iterative reasoning-and-action loop in which an AI model assesses a task, selects and invokes tools, interprets the resulting observations, and repeats until it can answer or reaches a limit. Commonly implemented in frameworks such as LangChain and LangGraph, the approach supports practical applications including customer support, coding assistants, and research tools, while offering more real-world grounding than reasoning-only methods. Compared with alternatives such as plan-and-execute, ReWOO, Reflexion, and multi-agent systems, ReAct is relatively simple and flexible but can require frequent model calls and only plans one subtask at a time. Effective operation depends on managing prompt-based working memory, thread-level persistence, and long-term cross-conversation memory, since repeatedly sending an expanding history can raise token costs quadratically, increase latency, and introduce context-related errors. The piece argues that trimming, summarization, retrieval, caching, and a consolidated real-time context layer can help keep ReAct systems accurate, responsive, and economical at production scale, presenting Redis Iris and related Redis services as tools for those functions.

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