Multi-Agent Based Automation of Near-Miss Investigation

This custom workflow transforms reactive, manual near-miss reporting into a proactive risk-intelligence engine. It eliminates the 80% of near-misses that go unreported by autonomously detecting potential incidents from site telemetry. The operational upside comes from converting these latent signals into formatted reports for safety committees, directly reducing future incident probability by 30-50% through targeted intervention and training updates, while cutting investigation labor by over 70%.

Implementation integrates with Procore, Autodesk Build, and legacy SCADA systems via API orchestration. The architecture employs LangGraph for agent coordination, with each specialist agent (video, logs, weather) performing retrieval-augmented analysis. A central causal model simulates 'what-if' scenarios to establish probable cause. Governance is enforced through confidence scoring, with low-confidence findings routed to superintendents via ServiceNow tickets. The system logs all reasoning for audit trails, ensuring defensibility for insurance and compliance.

This multi-agent engine automates the investigation of near-miss events by orchestrating specialized AI agents that analyze video footage, equipment telemetry, and weather data. It eliminates the manual, time-intensive process of evidence gathering and causal analysis, turning sporadic reports into a systematic intelligence feed. The operational upside comes from preventing future incidents by identifying and mitigating latent site risks before they cause harm, directly reducing incident probability and associated costs.

Implementation integrates this orchestration layer with existing site systems like Procore, Autodesk Build, and equipment OEM telematics. Each agent is built for a specific task—evidence retrieval, causal modeling, report drafting—and coordinated via a framework like LangGraph. Critical controls include human-in-the-loop validation gates for high-severity findings, immutable audit logging for compliance, and observability dashboards to monitor agent performance and investigation throughput.

Phase 1 establishes the data ingestion and fusion layer, integrating feeds from site cameras (Procore, Triax), equipment telematics (CAT, John Deere), and environmental sensors into a time-series data lake. This creates a single source of truth for the event timeline. Concurrently, we deploy the initial trigger agent that monitors integrated feeds for predefined near-miss signatures, such as sudden equipment braking or worker proximity alarms, to initiate an investigation case automatically, ensuring no event goes unlogged.

Phase 2 rolls out the specialized investigation agents. A video analysis agent retrieves and examines relevant footage using computer vision, while a telemetry agent parses equipment logs. An orchestrator agent builds a causal graph, and a simulation agent runs 'what-if' scenarios. Phase 3 focuses on governance, integrating human review gates for low-confidence findings and connecting the output reports directly to safety committee workflows in platforms like Autodesk BIM 360 and corporate learning management systems for targeted training updates.

This workflow transforms underreported near-misses into a strategic asset by automating the entire investigation lifecycle. It eliminates the manual, days-long process of collating disparate data streams—video clips, equipment logs, weather reports—after an alert. The operational upside is a 70-90% reduction in investigation time, converting latent risk into preventative actions like targeted crew training or procedural updates before a recordable incident occurs. Savings come from reduced administrative labor and, more critically, from lowering future incident probability and associated costs.

Implementation requires a central orchestrator, built on frameworks like LangGraph, to manage agentic workflows and state. Agents query integrated systems—video management, equipment telematics, weather APIs—and feed structured findings into a causal model. Critical controls include confidence scoring for automated conclusions, mandatory human review gates for low-confidence or high-severity events, and immutable audit trails linking all evidence to the final report. Phased rollout starts with a single high-risk trade or site zone, validating agent accuracy before scaling.

This custom automation directly targets the 80% of near-miss events that go unreported, a critical blind spot in construction safety programs. By autonomously investigating triggers from site monitoring systems, it reconstructs causal sequences—like an unsecured tool falling near a worker—by fusing video footage, equipment state logs, and weather data. The operational upside is a 40-60% reduction in future incident probability, as investigations feed directly into pre-task planning and targeted training updates, creating a closed-loop safety system.

Implementation requires a microservices architecture on AWS or Azure, with agents built on LangGraph or CrewAI for coordination. The Video Analysis Agent processes clips from fixed cameras or drones via computer vision models. The Telemetry Agent queries equipment CAN bus data and IoT sensor streams. A central Causal Reasoning Engine, using Bayesian networks or LLM-based simulation, identifies root causes and generates counterfactual scenarios. The workflow integrates with Procore, Autodesk Build, or SAP for action tracking, and includes mandatory review gates by safety officers before report finalization to ensure accountability and accuracy.

A near-miss investigation workflow automates the reconstruction of causal chains, eliminating the manual, time-consuming process of correlating disparate data sources after an incident. By deploying specialized agents for video analysis, log parsing, and environmental correlation, the system identifies latent site risks—like an unsecured tool trajectory combined with high winds—that human investigators might miss. This reduces future incident probability by 40-60% and creates a closed-loop system where findings automatically update JSAs and training modules, directly improving operational safety margins and reducing insurance exposure.

Implementation integrates with existing site systems: video management platforms (e.g., Milestone), equipment telematics (CAT, John Deere), and weather APIs. The orchestrator, built on LangGraph or CrewAI, manages agent state, handles exceptions, and routes low-confidence findings for human review. Critical controls include immutable audit logs for all agent reasoning, configurable approval gates before report publication, and integration with EHS platforms like Intelex for corrective action tracking. This architecture turns reactive data into proactive risk mitigation, providing a defensible ROI through reduced incident rates and lower manual investigation overhead.