The Agentic Scratchpad: Why Your LLM Needs a Cache Tool

LLM developers face significant challenges with context window limitations, leading to excessive costs and latency issues. To address these, a working memory architecture using an Agentic Scratchpad or Session Cache is proposed, which stores heavy data externally and retrieves only necessary information, significantly reducing token usage and improving response times. This approach ensures more accurate and efficient LLM performance in enterprise applications.

Read the full article at Towards AI - Medium

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