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# How to Build Deep Agents for Enterprise Search with NVIDIA AI-Q and LangChain

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 9 min read · 1 upvotes · 0 comments

## Summary

A step-by-step guide to deploying the NVIDIA AI-Q blueprint with LangChain to build enterprise deep research agents. Covers setup with Docker Compose, configuring shallow and deep research agents using Nemotron and GPT models, monitoring execution traces with LangSmith, and extending the system with custom enterprise data sources via the NeMo Agent Toolkit. The deep agent architecture uses a planner-researcher sub-agent pattern with structured context passing to avoid token bloat and improve multi-step reasoning quality.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/how-to-build-deep-agents-for-enterprise-search-with-nvidia-ai-q-and-langchain/>

---

Tags: [#python](https://daily.dev/tags/python), [#nvidia](https://daily.dev/tags/nvidia), [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#langchain](https://daily.dev/tags/langchain)

[View this post on daily.dev](https://daily.dev/posts/how-to-build-deep-agents-for-enterprise-search-with-nvidia-ai-q-and-langchain-t61dheiyx)

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