Multi-Agent Based Automation of Wearable IoT Sensor Data

This custom workflow automates the continuous monitoring of worker exposure to noise, dust, and toxic gases by orchestrating data from wearable IoT sensors like dosimeters and gas detectors. It eliminates manual data collection and log-keeping, reducing administrative labor by over 70% and ensuring real-time compliance with OSHA 29 CFR 1910 and 1926 standards. The operational upside comes from preventing costly citations and health incidents by triggering alerts when individuals or crews approach permissible exposure limits (PELs), enabling proactive intervention.

Implementation requires a multi-agent architecture on a platform like LangGraph, where specialized agents handle device health, data normalization, and threshold analysis. The system integrates with EHS platforms and ERP systems (e.g., SAP, Oracle) for automated recordkeeping. Critical controls include configurable PELs per substance, human-in-the-loop approval for high-severity alerts, and immutable audit logs for regulatory defense. Rollout must sequence by trade crew and sensor type, starting with high-risk activities like silica work or confined space entry.

This workflow eliminates manual data collection and spreadsheet-based exposure tracking, directly automating OSHA 300 log entries and reducing administrative labor by over 80%. It transforms reactive, post-incident compliance into proactive risk management by analyzing real-time biometric and environmental streams. The operational upside comes from preventing costly citations, reducing workers' compensation claims related to overexposure, and providing defensible audit trails that lower insurance premiums and litigation risk.

Implementation integrates with EHS platforms like Cority or Intelex and CMMS like IBM Maximo via APIs. The architecture uses a time-series data lake (e.g., InfluxDB) for sensor telemetry, with LangGraph orchestrating specialized agents for analysis, alerting, and system updates. Critical controls include human-in-the-loop approval for high-severity alerts, configurable exposure thresholds per trade, and continuous model validation against ground-truth medical data to prevent false negatives that could lead to regulatory exposure.

This workflow automates the ingestion, normalization, and analysis of data from a fleet of wearable IoT sensors—including gas detectors, dosimeters, and heart rate monitors—to enforce exposure limits and predict safety incidents. It eliminates manual data logging, reduces OSHA citation risk, and provides a continuous operational picture of crew safety status. The business case is built on preventing costly downtime from health incidents, automating compliance reporting, and improving workforce utilization through predictive safety interventions.

Implementation follows a phased, risk-managed approach. Phase 1 establishes the data pipeline and validation agent, integrating with existing site telemetry. Phase 2 deploys the analysis and alerting agents with human review gates. Phase 3 adds predictive modeling and closed-loop integration with workforce management systems like Procore. Each phase includes controls for data quality, exception routing, and audit trails to ensure operational confidence and regulatory defensibility before scaling.

Deploying this workflow requires a phased rollout to manage operational risk. Start with a single crew and one sensor type, such as gas detectors, to validate the data ingestion pipeline, alert accuracy, and supervisor response protocols. This controlled launch isolates integration challenges with systems like Autodesk Build or Oracle Primavera and establishes a baseline for OSHA exposure recordkeeping before scaling. The initial phase must include comprehensive logging of all agent decisions and sensor health metrics to build an audit trail.

Governance is enforced through immutable logging, configurable approval gates, and continuous monitoring of agent behavior. All exposure data and alert triggers are written to a tamper-evident ledger, creating a defensible record for OSHA audits. Supervisors receive alerts with recommended actions but retain final authority, with any override requiring a documented rationale. The system's performance, including false-positive rates and sensor uptime, is continuously evaluated against predefined KPIs, with automated rollback protocols if agent confidence scores fall below thresholds, ensuring safety is never compromised for automation speed.