Building an AI Agent to Detect and Handle Anomalies in Time-Series Data

An AI agent can be built to detect and handle anomalies in time-series data, moving beyond traditional static methods. The proposed system combines statistical techniques like Z-scores for initial detection with an intelligent agent for contextual decision-making. Using live COVID-19 case data as an example, the agent classifies anomalies as critical, warning, or minor based on their severity. Based on this classification, the agent autonomously decides whether to fix the data point, keep it as a valid signal, or flag it for human review. This agentic framework provides a more dynamic and intelligent solution for data monitoring compared to traditional, purely statistical approaches.