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Counterintuitively, the more context that an agent has, the worse the response quality becomes, since it becomes more difficult for the LLM to parse the signal from the noise. Note, this is not a problem that can be solved by simply increasing the size of a context window; that actually can make it worse. The larger the context, the worse the dilution of key instructions or context becomes, leading the model’s attention mechanism to spread its “focus” across more tokens. To combat this problem, Agents are now relying more heavily on some form of external state management (often called Memory), which is a continuously curated context that can be injected into the generation process as needed.
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