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    Deep Agents harness moves context out of the active window without deleting it

    LangChain says agents take in more instructions, tool results, files and drafts as they run, reducing accuracy and reasoning.

    LangChainLA
    1 Source, 1h ago, first seen 1h ago

    TLDR

    LangChain describes its Deep Agents harness as moving context out of an agent's active window without deleting it. The company says this addresses a problem with longer-running agents: as they take in more instructions, tool results, files and drafts, their accuracy and reasoning decline.

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    1 Source, first seen 1h ago

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    Combined views

    3.7K

    1 Source, first seen 1h ago

    30 likes8 comments25 saves4 reposts

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    1 Source

    LangChain@LangChainHow do agents remember? The longer an agent runs, the more it takes in (instructions, tool results, files, and drafts), which results in decreased accuracy and reasoning. Read how LangChain's Deep Agents harness addresses this by moving context out of the active window without deleting it ⤵️1h
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    1 Source

    LangChain@LangChainHow do agents remember? The longer an agent runs, the more it takes in (instructions, tool results, files, and drafts), which results in decreased accuracy and reasoning. Read how LangChain's Deep Agents harness addresses this by moving context out of the active window without deleting it ⤵️1h
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