Deploy a LUMOS Multi-Agent System for Continuous Model Drift Monitoring in Production

Introduction Production AI models are not static. Over time, data distributions shift, user behavior changes, and underlying systems evolve — a phenomenon known as model drift. If left unchecked, drift can silently degrade accuracy, increase latency, or cause citation decay in retrieval-augmented generation (RAG) pipelines, leading to poor business outcomes. Traditional monitoring methods are often reactive, requiring manual inspection or scheduled retraining that misses intermittent issues. A multi-agent system built on the LUMOS platform offers a proactive, continuous approach. In this guide, you'll learn how to deploy a LUMOS multi-agent system that monitors model drift in real time. We'll define agent roles, set up a baseline comparison workflow, implement drift scoring, and configure automated alerting — all using a supply chain demand forecasting scenario as a concrete example. By

the end, you'll have a reusable framework to keep your models reliable and maintain trust in your AI-driven operations. Understanding Model Drift in Production Model drift manifests in several forms: - Data drift : Changes in the input data distribution (e.g., holiday season spikes in demand). - Concept drift : Shifts in the relationship between inputs and outputs (e.g., new customer preferences). - Latency drift : Gradual increase in inference time due to infrastructure or model changes. - Citation drift : For RAG systems, when retrieved documents or generated citations become less relevant over time. Each type can degrade performance without immediate errors. A robust monitoring system must detect these shifts continuously and trigger appropriate actions before end users are affected. The LUMOS Multi-Agent Framework for Drift Detection LUMOS provides a flexible agent orchestration envi

ronment where specialized agents collaborate. For drift monitoring, we define five core agents: 1. Collector Agent : Gathers real-time metrics from production inference endpoints — input features, predictions, latency, and retrieval quality scores. 2. Baseline Agent : Maintains a reference distribution or performance profile computed during a known-good period (e.g., first month after deployment). 3. Scorer Agent : Compares live metrics against the baseline using statistical tests (e.g., Kolmogorov-Smirnov, Jensen-Shannon divergence) and updates a drift score for each metric. 4. Alert Agent : Evaluates scores against thresholds and sends notifications (email, Slack, PagerDuty) when drift exceeds acceptable limits. 5. Logger Agent : Records all metrics, scores, and alerts to a persistent store for audit and retraining decisions. These agents communicate via LUMOS’s message bus, enabling a

synchronous, decoupled execution. Each agent can be scaled independently based on load. Step-by-Step Example: Supply Chain Demand Forecasting Consider a retail company that uses a machine learning model to forecast daily demand for thousands of SKUs across warehouses. The model was trained on three years of historical data and deployed six months ago. Recently, inventory planners noticed occasional overstocking of certain items. We'll implement a LUMOS multi-agent system to monitor drift. Prerequisites - A LUMOS runtime environment (cloud or on-premises) with agent SDK installed. - Access to the production model's inference API and logging system. - Historical baseline data from the first 30 days after deployment. Step 1: Define the Baseline Agent The Baseline Agent loads historical data and computes summary statistics (mean, standard deviation, percentiles) for key input features (e.g.,

price, promotion flags, day of week) and output predictions (units forecasted). Step 2: Implement the Collector Agent The Collector Agent subscribes to the model's inference topic and extracts every prediction event. It enriches the event with a timestamp and model version, then publishes the metric snapshot. Step 3: Build the Scorer Agent The Scorer Agent subscribes to raw metrics and the baseline. For each metric (e.g., feature distribution, prediction average), it runs a statistical test and produces a drift score between 0 and 1 (0 = no drift, 1 = extreme shift). Step 4: Configure the Alert Agent Define thresholds per metric. For example, a drift score above 0.8 on demand predictions triggers a high-priority alert. The Alert Agent receives scores and sends notifications. Step 5: Wire Everything Together in LUMOS Define a pipeline that connects agents in the correct order. LUMOS orch

estrates message flow automatically. Deploy the pipeline using . The system begins monitoring immediately. Interpreting Results After a week, your team receives an alert: the drift score for the “promotion flag” feature hit 0.85. Investigation reveals a new marketing campaign that doubled promotiona