People sometimes ask how we deal with model drift at Aampe.
We don’t, for the most part. We don't have to. Agentic systems that operate on the basis of semantic-associative learning don’t have to worry about drift in the traditional sense, because the system learns continuously.
Model drift is a problem you get when you deploy a static model and hope it stays useful. It’s rooted in an old deployment pattern: train a model offline, push it into production, and re-train it periodically when performance drops. That setup invites drift because the world keeps moving while the model stands still.
Agentic infrastructure doesn't involve the deployment of fixed models. Each agent is continuously updating based on real-time user interactions. Every user has their own evolving policy, shaped by live feedback.
Instead of worrying about drift, we focus on responsiveness:
Making sure exploration stays active
Adapting quickly to behavioral shifts
Keeping up with changes in reward signals
Those adaptations aren’t on a quarterly retraining schedule - they happen constantly. Every agent's parameters are updated at least daily.
Traditional ML setups come with more failure points than people often realize. Monitoring isn’t automatic—you have to build it, maintain it, and decide what exactly to watch for. Then, when performance starts slipping, someone has to make a judgment call: Has the model drifted enough to justify retraining? And once that decision is made, retraining itself is a whole new project. In contrast, an agentic system doesn’t treat learning as an occasional intervention. It’s built to act and adapt continuously from day one.
Feedback isn’t a separate process—it’s baked into the core loop.
