Workforce Analytics & Intelligence Digital Worker

Core Logic

## GPT-4o Powered Coordination Initializes and monitors all agent states, creates workflow execution plans based on scenario type, routes tasks through dependency-aware sequencing, handles inter-agent message passing, implements error recovery and retry logic, and aggregates outputs into comprehensive WorkflowOutput structures. Maintains workflow metrics including duration, agent invocations, data points processed, and estimated cost savings.

Core Logic

## Enterprise Data Pipeline Supports API connectivity, database extraction, and file ingestion with automatic schema detection. Performs incremental loading to minimize processing time, validates data quality with scoring metrics, and outputs normalized records to the feature store. Tracks pipeline metrics including total records, throughput, latency, and data quality scores across five-stage ETL process (Extract, Validate, Transform, Enrich, Load).

Core Logic

## Automated Feature Pipeline Performs feature extraction from raw data, applies normalization and scaling transformations, handles categorical encoding, synchronizes with feature store for model serving, and maintains feature lineage documentation. Outputs ML-ready feature sets with quality validation and versioning.

Core Logic

## Time-Series Forecasting Engine Applies Holt-Winters Triple Exponential Smoothing with hour-of-day, day-of-week, and seasonal pattern decomposition. Generates predictions with confidence intervals (lower/upper bounds), identifies seasonal patterns (Morning Peak, Evening Surge, Weekend Dip, Monday Spike), detects anomalies with suggested actions, and maintains model metadata including accuracy metrics (MAPE 7.7%), drift status, and feature importance.

Core Logic

## XGBoost Classification with SHAP Explainability Scores all employees using XGBoost classifier (AUC-ROC 0.847) with SHAP-based factor explanations showing contribution direction and magnitude. Outputs risk levels (Critical/High/Medium/Low), top contributing factors (scheduling trends, engagement, pay competitiveness), personalized intervention recommendations, retention probability estimates, and replacement cost calculations. Generates cohort analysis by tenure bands and comprehensive intervention plans.

Core Logic

## Erlang-C + Linear Programming Optimization Applies Erlang-C queuing models combined with linear programming constraint satisfaction to optimize staffing. Compares current vs. optimized state metrics (headcount, utilization, SLA, cost per contact, overtime), generates specific recommendations (hire/reduce/shift/train/reallocate) with cost/savings analysis, produces schedule change recommendations by day/time slot with business justification, and calculates comprehensive cost analysis with category breakdowns.

Core Logic

## Q-Learning Reinforcement Learning Engine Applies epsilon-greedy Q-Learning policy to optimize shift pricing based on historical fill rates, time-to-shift, market conditions, and demand patterns. Generates per-shift recommendations with current rate, recommended rate, adjustment percentage, confidence score, and reasoning explanation. Includes market analysis (competitor rates, demand/supply trends) and revenue impact projections with margin analysis.

Core Logic

## AI-Powered Narrative Generation Synthesizes findings from forecasting, attrition, optimization, and pricing agents into categorized insights (Opportunity/Risk/Trend/Anomaly) with impact severity, confidence levels, and supporting data. Generates executive summaries with headlines, key findings, prioritized action recommendations with owners, financial impact analysis, risk assessments with mitigations, and AI-generated narrative reports (Executive Briefings, Action Plans, Risk Briefs) with reading time estimates and recommended audiences.