Adaptive Drift Defense: A Unified Framework for Data, Task, And User-Intent Drift in LLM Apps

Abstract

The deployment of Large Language Models (LLMs) to production systems where user expectations, data source and task requirements are constantly changing is increasing. This change brings in the element of drift--changes to input distributions, tool-call patterns or user intent--that impairs performance given enough time and is passed unnoticed. Current approaches to drift management tend to be either excessively specific to data-level drift monitoring or directly retrain the model which represents an unacceptable resource-intensive task; thus, much ground remains to be lost with regards to real-time response to drift and resource requirement.

In this paper we introduce coherent framework Adaptive Drift Defense, which can integrate three orthogonal layers of detection, like retrieval distribution monitoring, tool-call graph analysis or estimation of output variance, to detect data, task and user-intent drift in a concurrent way. A computing bandit mitigation policy actively chooses timely refinements, retrieval adaptations or tool-routing policies, driving performance to equilibrium in terms of requiring retraining of the model.

Experiments on major customer care helpers (~1.2M interactions) and business analytics copilots (~800k queries) show that the system achieves an 88 percent accuracy and 86 percent recall in detecting drift, with 20-35 percent savings in the cost of manual rework with insignificant latency overhead (<50ms). When compared to non-incremental pipelines, task success rates were increased by 15-20 percent, bridging most of the performance-gap to full retraining with only 12-percent extra compute cost.

These results imply the conceptual feasibility of conventional, collaborative drift monitoring of LLM system. Analytical paper also furnishes reference dashboards and operational playbooks which assists deployment teams. These results help us understand that adaptive methods with mitigation-first approaches will enable high-quality service quality in highly dynamic settings and that it scales well to situations of frequent model retraining.