Two people argue that agentic AI's biggest bottleneck is capturing rich, contextual data. They say model improvements and better prompts only multiply what's already there. Traditional data warehouses preserve the wrong signals, and without actively collecting context, AI agents can't make effective decisions.
Yeah. So the importance, there's like where data may have been not as important and you don't, you didn't have leverage over it now becomes exceedingly important. So if there's one takeaway, it's get the data, you know, start catching as much data as you can because the more context that you have, the more you can leverage the machine to help you.
Right. I think that that's the key for most folks. I have found they focus a lot on how can we refine it better? How can we have better models? How can we have better prompts, whatever?, but it's all a multiplier, a multiplier on the data. And when they're not collecting it and they're just letting it fritter away, which many are, uh, that is, I think, the, uh, the, the, the key missing part. That's where like a lot of, you know, opportunity is for, you know, uh, you know, the robots to be able to come in and be able to make sure this stuff doesn't, um, doesn't float away because traditional data warehouses haven't been solving this problem so far, not because they're structurally incorrect, but because they've been saving the wrong stuff.
