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How AI Agent Memory Works

This article explores how memory is implemented in AI agents, from simple context windows to semantic search, taxonomy, retrieval pipelines, and tradeoffs of different architectures.

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How AI Agent Memory Works
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Summary only

Under the source content permission, this page provides an editorial summary, key points, and a link to the original article.

Key points and analysis

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EngineersIntermediate

Key points

  • LLMs are stateless; agents use memory loops to carry information forward
  • Simplest memory: FIFO context window, but limited by context length
  • Working memory for real-time reasoning, long-term memory for persistence; orchestration is key
  • Production retrieval pipeline includes need detection, query rewrite, hybrid retrieval, reranking, and filtering

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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