Does drift only affect models you train yourself?
No. It also affects systems built on third-party models, because that model can change under you without notice.
Concept · Reliability
What is model drift, and how to catch it
Model drift is when an AI system's outputs quietly get worse over time as data, usage, or the model itself changes. Catching it early takes measurement, not vibes.
What drift looks like in practice
An agent that used to work starts failing in small ways: slightly worse answers, more manual corrections, edge cases it used to get right. Nothing breaks at once, which is exactly why it goes unnoticed.
- The input data shifts from what the agent was designed against.
- How people use it changes, with cases nobody planned for.
- The provider updates the model and behavior moves.
Why drift is silent
Drift does not throw an error or take a server down. The system keeps answering confidently — just worse. Without measurement, the first to notice is usually the customer, not the team.
- There is no visible failure to trigger an alert.
- The degradation is gradual and gets normalized.
- The output's apparent confidence hides the problem.
How to catch it early
The defense against drift is to quantify what 'good' means and watch it continuously. Paput defines thresholds and turns them into alerts.
- Trusted evals with an accuracy floor that must not drop.
- An override-rate ceiling: if people correct it too often, something has shifted.
- Continuous monitoring, not a one-off check at launch.
- Searchable logs to investigate what changed and when.
Questions buyers ask
How often should you measure?
Continuously for production workflows. A single check at launch will not catch degradation that shows up weeks later.
AI operator field notes
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