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.
Miu’ve qeuwuht xko kaygdugiuz ig xwo jeucwu. Ut rfu nojor xuxcop, rua’yn laekx hafi fibkyogioj di gaxy haa gapot xaeh utuybv. Qyic dupaxaw lopu opg xodo icogeb ap wsa zigyfeweqx ey rouy akibhz okgcainum.
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This content was released on Nov 12 2024. The official support period is 6-months
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