Behavioral Crisis Prevention Platform
Deploys a multi-agent AI system with real-time behavioral monitoring, predictive risk scoring using logistic regression ensembles (AUC 0.96), and evidence-based intervention recommendations.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Data Input Portal - Behavioral data submission with validation steps for student selection, ABC records, environmental data, and HIPAA compliance checks
Agent Orchestration - Real-time multi-agent workflow visualization with active agent network, tool calls, and data enrichment pipeline
Results & Insights Hub - AI-generated crisis risk predictions with risk score, probability forecast, trajectory chart, and contributing factors analysis
Executive Dashboard - Business impact metrics showing crises prevented, cost savings, staff time saved, risk profile distribution, and system health status
AI Agents
Specialized autonomous agents working in coordination
Master Workflow Orchestrator
Complex behavioral analysis requires coordinating multiple specialized AI agents in precise sequences. Without central coordination, agents may produce conflicting outputs, miss critical handoffs, or fail to aggregate insights into actionable predictions.
Core Logic
Serves as the master coordinator managing an 11-step sequential workflow. Implements message-based inter-agent communication with correlation IDs, priority routing, and TTL management. Maintains working memory (7-item capacity) and episodic memory for contextual decision-making. Executes chain-of-thought reasoning with goal-observation-hypothesis-action patterns for transparent decision paths.
Data Validation Agent
Behavioral data ingestion involves diverse sources with inconsistent formats, missing fields, and potential quality issues. Invalid data propagating through the pipeline produces unreliable predictions that could miss genuine crises or generate false alarms.
Core Logic
Performs schema validation against predefined behavioral data structures and applies business rules validation. Tools include schema-validation with configurable rulesets. Flags outliers, validates required fields (student ID, timestamp, behavioral indicators), ensures data type consistency, and generates validation reports with error categorization. Achieves 98%+ data quality scores.
Behavioral Pattern Analysis Agent
Behavioral incidents follow patterns that human observers often missβseasonal variations, time-of-day correlations, environmental trigger sequences, and gradual escalation signatures hidden across weeks of data.
Core Logic
Executes time-series analysis using 7-day Exponential Moving Averages (EMA) for trend detection. Implements anomaly detection algorithms to identify deviations from baseline behavior. Tools: time-series-analysis, anomaly-detection. Generates structured observations including trend direction, seasonality coefficients, and moving average calculations with confidence levels.
Crisis Risk Prediction Agent
Determining crisis probability requires synthesizing multiple data streamsβhistorical incidents, current behavioral state, environmental factors, and pattern analysisβinto a single actionable risk score with meaningful confidence bounds.
Core Logic
Employs logistic regression ensemble models (94.7% recall, 89.3% precision, 0.96 AUC) to generate crisis probability predictions. Outputs: RiskScore (0-100), riskLevel classification, crisisProbability (0-1), timeToEvent in minutes. Produces escalation trajectories with 15/30/60-minute forecasts and confidence intervals. Inference latency: P50=23ms, P95=45ms.
Intervention Recommendation Agent
Selecting appropriate interventions from hundreds of options requires matching student-specific factors, situation context, historical effectiveness, available resources, and contraindicationsβa combinatorial challenge exceeding human cognitive capacity in crisis moments.
Core Logic
Uses collaborative filtering and K-NN matching algorithms to rank interventions by predicted success rate. Outputs evidence-based recommendations (e.g., Sensory Break: 89%, Movement Break: 83%) with resource requirements, duration estimates, contraindications, and evidence base (study count, sample size, effect size, quality score). Tools: intervention-ranking, K-NN-matching.
Compliance & Audit Agent
Behavioral crisis interventions must comply with HIPAA, FERPA, SOC2, and EU AI Act regulations. Manual compliance checking is time-consuming, error-prone, and creates audit gaps that expose organizations to legal and regulatory risk.
Core Logic
Validates all predictions and recommendations against regulatory frameworks before output. Generates comprehensive audit records with 50+ event types (data-access, model-prediction, agent-action, data-modification). Maintains data lineage with transformation tracking (anonymization, enrichment, aggregation). Enforces retention policies (FERPA: 2555 days for PHI). Produces compliance reports with violation flagging.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization