Unverified50% confidenceFactExact time
Agents invoke tools, access external data, perform multi-step reasoning, and dynamically adjust their behavior based on environmental feedback, which differs from how static models operate
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50%
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Long-term
Relevance
9/28/2026
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Unverified智能体工作负载系统通常基于规划-执行-反馈循环，底层依赖大语言模型的链式推理和工具调用能力76% similarVerifiedTool calling allows models to dynamically invoke external functions during inference and incorporate execution results into the context of subsequent reasoning, forming a closed loop of perception, decision-making, and execution.74% similarUnverifiedReAct tightly couples the model's internal reasoning process with external tool calls, achieving significantly higher accuracy than pure reasoning approaches on tasks requiring real-time information queries, computation execution, or interaction with external APIs.72% similarUnverifiedThere are two paths to getting the model to output an accurate solution: writing detailed requirements all at once, or using follow-up questioning to have the model think on the user's behalf and accumulate context.72% similarVerifiedThe ReAct framework enables the model to interact with the external environment while reasoning—first reasoning about what to do, then calling external tools to act, then observing the results returned by the tools, iterating in a loop until the task is complete.71% similar
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