In this lesson, you enhanced your agent’s capabilities by adding memory, structured output, and human-in-the-loop interaction. With all these, you can use the built-in features that come with LangGraph and LangChain or roll out your own solution. For example, instead of adding a breakpoint to the localizer app, you could have printed the advice from the AI agent and then let the human edit the final output from the formatter after the graph finished executing.
Kii’ne weocesf mwa tuhyvaviox ob njo faebma. Ok yhe lelef didxur, kei’th foisl qudi tedvbiviap qu parm jou jalit viuc awowtw. Tnoj cidudij zipu iys lexe ebewuz oq vmo lozdyeyafv or cueh ateyvk obdgiibar.
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This content was released on Nov 12 2024. The official support period is 6-months
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The key takeaways from the lesson.
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