AI-Powered At-Risk Learner Identification & Intervention Platform

Core Logic

Manages the end-to-end campaign workflow through 5 phases: initialization, data analysis, risk prediction, strategy design, safety validation, and recommendation generation. Delegates tasks to specialized agents with context, monitors progress, handles errors, aggregates outputs, and manages transitions between workflow steps. Tracks comprehensive system metrics including tokens, cost, latency, throughput, and autonomous decisions. Maintains workflow state allowing pause/resume and step navigation.

Core Logic

Queries learner databases to retrieve profiles matching target criteria, analyzes engagement metrics (login frequency, time on platform, content interactions), compares learners against peer cohorts, identifies common patterns (quiz failure spiral, weekend dropout, manager pressure), retrieves historical success patterns from episodic memory, and provides context for downstream agents. Capabilities include pattern recognition, anomaly detection, trend analysis, and cohort comparison.

Core Logic

Extracts multi-dimensional feature vectors (progress velocity, engagement trend, quiz performance, time on platform), applies logistic regression models calibrated on millions of learner journeys for completion probability prediction, uses Kaplan-Meier survival analysis for churn prediction over configurable horizons (e.g., 30-day), provides confidence scores and uncertainty estimates, and identifies learners matching high-risk patterns. Model confidence typically exceeds 94% with continuous calibration.

Core Logic

Designs personalized interventions by selecting optimal strategy type (personalized email, manager alert, content recommendation, in-app notification, peer connection, schedule adjustment, support call), generating personalized message content using learner context (name, course, progress, stuck module), optimizing channel selection based on historical response rates, predicting effectiveness (open rate, response rate, recovery probability), and scheduling interventions for optimal engagement windows. Applies learned patterns from 847+ historical intervention episodes.

Core Logic

Enforces comprehensive guardrails including PII detection (scanning messages for unauthorized personal data with high sensitivity), bias detection (ensuring intervention distribution is proportional across demographics), content moderation (toxicity filtering for appropriate tone), budget validation (checking costs against daily/per-learner limits), rate limiting (enforcing maximum contact frequency), and confidentiality checks. All checks are logged with pass/warning/blocked status, severity levels, and detailed explanations. Violations block recommendations from proceeding to human review.

Core Logic

Manages four-tier memory architecture: Working Memory (current context, scratchpad, active goals, recent decisions), Episodic Memory (past interventions with outcomes, learner interactions, success/failure patterns), Semantic Memory (domain knowledge, learner profiles, content embeddings, intervention strategies), and Procedural Memory (learned behaviors, optimized workflows, personalization models). Provides vector search for semantic retrieval, pattern matching for historical precedents, and context building for agents. Enables knowledge transfer between agents during collaboration.

Core Logic

Monitors real-time signals including market trends (technology, methodology, compliance, skills demand with impact scores), industry benchmarks (completion rates, engagement scores, retention rates, satisfaction, ROI with percentile rankings), competitor insights (personalization, mobile learning, social learning gaps), learner sentiment analysis (overall sentiment, emotion breakdown, topic analysis), and generates predictive alerts (risk, opportunity, anomaly, milestone types) with confidence scores and suggested actions. Supports autonomous action execution (auto-intervention, escalation, resource allocation, schedule optimization) with human override capabilities.

Core Logic

Generates multi-scenario forecasts (optimistic, baseline, pessimistic) with probability distributions and driver analysis, projects risk trends with mitigation impact estimates, predicts capacity utilization with peak dates and resource recommendations, performs cohort analysis (by enrollment cohort, department, program) with performance-vs-benchmark comparisons and predicted outcomes, and identifies seasonal patterns (end-of-quarter surge, holiday slowdown) with actionable recommendations. Supports configurable forecast horizons and confidence thresholds.