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This article addresses a critical bottleneck for AI adoption by detailing infrastructure tools essential for moving large language model agents from experimental notebooks to robust production environments. It specifically highlights LangGraph as a key framework that manages agent state, enables pause-and-resume capabilities, and supports complex workflows like human-in-the-loop approvals without requiring custom infrastructure.

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5 Tools for Building and Deploying AI Agents in Production

KDnuggetsAugust 19, 20267 min read

This article walks through five tools, one for each layer of the stack from building the agent's logic to running all of it at scale.

Building an agent that works in a notebook takes an afternoon. Getting that same agent to survive real traffic, recover from a crash at 3 am, and not leak someone else's data while it runs large language model (LLM)-generated code is a different job entirely, and it's the job most teams underestimate. Only a very small percentage of generative AI pilots actually reach production, and the gap usually isn't the model. It's the five layers underneath it that nobody thinks about until something breaks. This article walks through five tools that close that gap, one for each layer of the stack: building the agent's logic, executing the code it generates, giving it memory, watching what it does, and running all of it at scale. None of these competes with the others. They sit on top of one another, and most production agents…

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