ReAct pattern makes AI agents think-act-loop like pros, until they forget everything mid-chain just like your quarterly roadmap

On February 4, 2026, a session was held to discuss the ReAct pattern, a concept that stands for Reasoning + Acting, explicitly interleaved. Klover, an expert in the field, explained that ReAct involves a loop where a large language model (LLM) thinks, takes actions, observes results, and then thinks again. This process allows the model to decide when it has enough information to provide a final answer. ReAct is similar to chain-of-thought, but with actual tool use mixed in, enabling the model to call tools, such as search or calculators, and observe the results. The system uses stop sequences to intercept and execute tool calls, and the model is trained to output in a specific format. ReAct has two main failure modes: infinite loops and context overflow, which can be mitigated by setting a max iteration limit and compressing context between steps. LangChain's ConversationSummaryBufferMemory is a solution that compresses context, keeping recent messages raw and older ones summarized. This technology has significant implications for the development of more advanced and adaptive LLMs.