Build an AI Agent for Enterprise Research
AI-Q is NVIDIA’s blueprint for building a “deep research” assistant that produces long-form reports with citations. The flow is intentionally plan-driven: given a topic and desired report structure, the agent drafts a plan, runs multiple research questions in parallel, writes a report, reflects on missing pieces, and iterates until it can finalize the output with a list of sources.
The most interesting design choice is that it prioritizes your internal data first. Queries hit an on-prem RAG service (with a judge step to check whether retrieved results are relevant) and only fall back to web search if the agent can’t find enough signal. That’s a practical pattern for enterprise deployments where you want “use our docs” to be the default behavior, not a prompt-level suggestion.
What to try first: deploy it against a small internal corpus (even a handful of PDFs) and give it a report outline you actually care about (e.g., “competitive landscape” + “key risks” + “recommendations”). Evaluate two things: whether it properly cites internal sources (and doesn’t hallucinate citations), and how often the reflect-and-research loop improves the report without drifting off-topic.
Source listing: https://build.nvidia.com/blueprints?filters=publisher%3Anvidia