AI systems need continuous retraining to stay reliable because trust decays as products, markets, and customer profiles shift. Even a well-trained model becomes untrustworthy when context changes, so deployments require ongoing monitoring and recalibration to maintain confidence in outputs.
The one thing I will call out is I came up with this concept like earlier this week called calibration loops, because the challenge is to Neil, at your point, is how much can you really trust these things if you don't train them, right? But there's a problem with that too. If you don't train it, it's gonna suck. If you train it, you might think you're done, but you actually need to continue to train it. Because what if you change your product? Or what if your ICP changes or whatever? So I think whenever you build an agent, you need to have like a calibration meter at the top to show how much trust you've built. But that decays over time too, because the product or your business changes.
