SafeGuard AI - Autonomous Compliance Intelligence System
Implements a LangGraph + ReAct architecture with 6 core agents plus 5 dynamically-spawned specialist agents. Features inter-agent collaboration with consensus voting, human-in-the-loop governance checkpoints, full AI decision audit trails, and predictive ML-based risk scoring.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Control - Mission type selection, Irish healthcare regulatory framework activation, analysis scope configuration, and mission preview with professional counts
Agent Swarm Topology - Multi-agent orchestration details, parallel and sequential execution modes, memory configuration, and inter-agent messaging stream
Trace View - Mission timeline with event tracking, LLM requests and responses, orchestrator activation, and data collector operations
Executive Summary - Overall compliance score, key findings with NMBI verification stats, mission metrics, and analysis overview with professional counts
AI Agents
Specialized autonomous agents working in coordination
Coordinator/Supervisor
Complex compliance missions require dynamic coordination—spawning specialist agents on-demand, managing collaborations, handling checkpoints, and synthesizing results from multiple parallel analyses.
Core Logic
Plans mission execution and coordinates 6+ agent activities. Delegates tasks via delegate_task tool. Awaits agent completions and compiles results. Dynamically spawns specialist agents when complexity requires (spawn_specialist). Initiates inter-agent collaborations for consensus decisions. Manages 6-phase execution: initialization, data collection, analysis, risk assessment, action execution, and reporting.
API Integration & Data Retrieval Agent
Compliance verification requires data from multiple Irish healthcare registries—NMBI, Garda NVB, HIQA—plus internal shift histories and facility data. Manual data collection is slow and incomplete.
Core Logic
Queries NMBI registry for license verification (query_nmbi_registry). Fetches credentials across 8 credential types (fetch_credentials). Retrieves shift history for 14,523+ records (get_shift_history). Pulls facility data for compliance context (retrieve_facility_data). Verifies Garda vetting status (verify_garda_vetting). Checks HIQA standards alignment (check_hiqa_standards).
Regulatory Violation Detection Agent
Identifying compliance violations requires comparing professional credentials against regulatory requirements, detecting gaps, and mapping issues to specific HIQA standards. Manual analysis misses violations.
Core Logic
Checks credential validity against expiration dates and regulatory requirements (check_credential_validity). Analyzes compliance gaps by professional and department (analyze_compliance_gaps). Compares credentials to role requirements (compare_requirements). Maps violations to specific HIQA standards (map_to_hiqa_standards). Identifies 16 non-compliant professionals from 347 evaluated.
Predictive ML-Based Risk Scoring Agent
Not all compliance issues carry equal risk. Without predictive scoring, resources are misallocated—critical risks are under-addressed while minor issues consume attention.
Core Logic
Calculates ML risk scores using logistic regression (calculate_risk_score). Predicts incident probability within 30-day windows (predict_incident_probability). Analyzes contributing risk factors (analyze_risk_factors). Detects statistical anomalies in compliance patterns (detect_anomalies). Achieves 94.7% accuracy and 89.5% F1 score. Identifies 7 high-risk professionals and predicts 23% incident probability.
Corrective Actions & Notifications Agent
Compliance violations require immediate action—blocking non-compliant professionals, sending renewal notifications, escalating critical issues. Manual action execution is slow and inconsistent.
Core Logic
Sends notifications via multiple channels (send_notification). Updates professional compliance status (update_status). Creates escalations for critical issues with SLA tracking (create_escalation). Schedules follow-up actions with deadlines (schedule_followup). Blocks professionals with expired credentials (block_professional). Triggers credential renewal workflows (trigger_renewal). Requests human approval for high-impact actions via checkpoints.
Executive Summaries & Reporting Agent
Compliance findings must be communicated to executives, analysts, and technical staff with appropriate detail levels. Manual report generation is time-consuming and inconsistent.
Core Logic
Generates executive summaries with headline findings and key statistics (generate_summary). Creates visualization data for charts and dashboards (create_chart_data). Compiles prioritized recommendations with deadlines and owners (compile_recommendations). Produces full audit trails for regulatory submission (generate_audit_trail). Outputs 45-page PDF reports with executive and technical appendices.
Deep NMBI/CORU Verification Specialist (Dynamic)
Complex credential issues—expired NMBI licenses, scope violations, suspected fraud—require deep investigation beyond standard verification. Core agents lack specialized expertise.
Core Logic
Dynamically spawned when critical compliance issues are detected. Performs deep NMBI/CORU registry verification with cross-reference checks. Investigates registration history and renewal patterns. Detects potential credential fraud through anomaly analysis. Validates scope of practice against assigned specialties. Provides detailed verification reports for blocking decisions.
Coverage Gap Analysis Specialist (Dynamic)
Compliance verification may reveal staffing gaps—departments where compliance actions (blocking) reduce coverage below safe levels. Without analysis, patient safety is compromised.
Core Logic
Spawned when staffing gaps are identified during compliance actions. Analyzes coverage ratios by department and shift. Calculates safe staffing deficits (e.g., Tallaght ICU: 3-nurse gap). Recommends immediate hiring or agency coverage. Projects staffing needs accounting for compliance-driven removals. Coordinates with emergency response planning.
Safety Event Pattern Detection Specialist (Dynamic)
Safety incidents may correlate with compliance issues—near-misses involving non-compliant professionals, patterns in specific departments. Manual investigation misses systemic issues.
Core Logic
Spawned when safety patterns are detected during analysis. Performs root cause analysis on safety incidents. Identifies correlation between incidents and compliance status. Detects recurring patterns by professional, department, or shift type. Provides evidence for compliance enforcement decisions. Generates incident investigation reports.
AI Decision Validation Specialist (Dynamic)
AI-driven compliance decisions require validation to ensure fairness, accuracy, and regulatory alignment. Without auditing, AI bias or errors may cause harm.
Core Logic
Validates AI decisions for accuracy and fairness. Detects potential bias in risk scoring or recommendations. Audits decision reasoning chains for logical consistency. Ensures regulatory alignment of automated actions. Provides quality assurance certification for AI-driven compliance enforcement. Reviews human checkpoint outcomes for consistency.
ML-Based Shortage Forecasting Specialist (Dynamic)
Credential expirations and compliance actions create future staffing shortages that must be anticipated. Without prediction, facilities face preventable crises.
Core Logic
Spawned when multiple credential expirations are detected. Performs time-series forecasting on compliance-driven availability changes. Analyzes seasonality in credential renewal patterns (Q4 surge). Predicts shortage windows 30-90 days ahead. Recommends proactive recruitment or training to prevent gaps. Integrates with workforce optimization planning.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization