Multi-Dimensional Anomaly Detection Agent

The Anomaly Detection Agent implements an Isolation Forest algorithm for unsupervised multi-dimensional anomaly detection. It analyzes entry features including: billed rate, hours worked, total amount, narrative quality score, and timekeeper patterns. The algorithm identifies approximately 15% of entries as anomalies requiring review, with each flagged entry including anomaly scores, contributing factors, and comparison to baseline patterns. Self-correction capabilities allow the agent to refine its detection by cross-validating against historical false positive patterns.