Agentic AI is scaling fast. Data is exploding even faster.
And that creates a silent failure: AI agents can’t find the right information, at the right time, to make the right decision—and act on it.
This is a question I’ve been thinking about for a while.
Today at #NVIDIAGTC, in a discussion with Brian Bergholm at the Elastic booth, one idea clicked:
>> It’s not a model problem. It’s a retrieval problem.
The knowledge exists everywhere: documents, logs, dashboards, emails, and databases. But it is scattered, unstructured, and too slow to access when it matters.
And for agents, that’s fatal. Because agents don’t just generate answers, they decide and act.
If you break the retrieval, the whole system breaks. That’s why search is becoming the backbone of the agentic enterprise.
👉 What impressed me is how Elastic, with NVIDIA, is tackling this at scale by combining vector search with GPU acceleration. The impact is not theoretical:
- 12Ă— faster indexing
- 7Ă— faster merging
- 5Ă— better cost-adjusted throughput
In other words: Better search = smarter agents.
👉 If you’re at GTC (https://nvda.ws/4c9q4UZ), stop by the Elastic booth (#3200) to see how to turn your data into real business value.
https://lnkd.in/eFPvACDi
The move to production AI isn't just about the model—it’s about the data architecture behind it.
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