Unverified50% confidenceFactExact time
When a model enters agentic mode and makes autonomous decisions across multiple steps through tool calls, each step's output becomes the input for the next step, and errors or deviations can be amplified exponentially within the loop.
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50%
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Long-term
Relevance
10/11/2026
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Related Claims
UnverifiedIn each iteration of the loop, an agent needs to repackage the system prompt, conversation history, tool descriptions, and all outputs from previous steps before feeding them into the model, causing token consumption to grow linearly or even super-linearly with the number of steps.79% similarUnverified智能体的执行具有非确定性，同样的输入可能因模型的随机采样而产生不同输出，因此其恢复机制需要保存完整的执行轨迹和中间推理步骤79% similarUnverifiedAn agent is essentially a loop in code around a model: construct a prompt, hand it to the model for a decision, have code execute the tools and put the results back into the prompt, and repeat until the model produces a final answer78% similarUnverifiedThe inputs and outputs of each hop in a multi-agent chain can be observed independently, making fault diagnosis more granular than the internal reasoning process of a single large model.77% similarUnverifiedIn multi-step agent scenarios, the platform's orchestration capabilities often determine the final outcome more than the quality of a single model inference.77% similar
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