Multi-Agent Based Automation of Maintenance Technician Dispatch

This workflow automates the assignment of maintenance technicians to unplanned events, a critical bottleneck in hyper-personalized production where any line stoppage disrupts low-volume, high-mix schedules. It eliminates manual coordination delays by evaluating real-time signals—technician GPS, skill certifications, active workload, and parts availability—against the work order's requirements. The operational upside is measured in reduced mean-time-to-repair (MTTR), protecting line utilization and preventing costly delays to custom order fulfillment.

Implementation integrates with SAP PM, ServiceNow, or a custom CMMS via API. The orchestrator, built on LangGraph or CrewAI, sequences specialized agents that query systems of record. Controls include mandatory human review for safety-critical procedures (e.g., high-voltage lockout) and real-time monitoring of agent decisions for audit. Exception routing handles parts shortages or skill gaps, triggering automated procurement or escalation workflows to maintain response SLA.

This workflow automates the critical bottleneck of assigning the right technician to the right maintenance event, directly protecting line utilization and margin in hyper-personalized production. Upon work order creation in a CMMS like SAP or ServiceNow, specialized agents evaluate real-time signals: technician GPS location from mobile apps, certified skills from HR systems, current workload from dispatch queues, and parts availability from inventory management. The orchestration logic, built on frameworks like LangGraph, makes the optimal assignment decision in seconds, eliminating manual coordinator guesswork and reducing mean-time-to-repair (MTTR).

Implementation integrates with enterprise mobile platforms to push digital work packs containing interactive SOPs, 3D models from the digital twin, and pick lists for the parts crib. The architecture includes mandatory approval gates for high-risk procedures and real-time observability into dispatch status and repair duration via a centralized dashboard. Exception handling routes complex faults to senior engineers, while the closed-loop update to the CMMS and digital twin ensures asset history and line simulation models remain accurate, creating a maintainable system that scales with production variability.

Phase 1 establishes the core orchestration layer, integrating with the CMMS (e.g., SAP PM, IBM Maximo) to ingest work orders and with HR systems for technician skill and location data. A LangGraph-based orchestrator evaluates simple rules for initial dispatch, routing assignments to a human supervisor for approval via a mobile interface. This MVP delivers immediate visibility and lays the data foundation for automation, allowing for process validation and user feedback without disrupting existing maintenance operations.

Phase 2 introduces predictive maintenance integration and optimization agents. The system begins ingesting alerts from IIoT platforms and digital twins, automatically creating work orders. The dispatch logic evolves to optimize for technician skill, proximity, and current workload, reducing mean time to repair. A digital work instruction agent retrieves SOPs from document systems. Phase 3 adds full autonomy with parts inventory checking and automated lockout/tagout procedure generation, closing the loop with post-work feedback to continuously tune agent performance and dispatch accuracy.

The business value of automated dispatch is direct: it minimizes machine downtime that disrupts custom production schedules, protecting margin on low-volume, high-value orders. The workflow automates the repetitive, time-consuming bottleneck of a supervisor manually matching a work order to a technician based on skills, location, and workload. Savings come from faster mean-time-to-repair (MTTR), higher technician utilization, and preventing costly line stoppages during personalized production runs. Implementation requires integrating with CMMS (like IBM Maximo or SAP PM), GIS for location, HR systems for skills, and real-time telemetry from the digital twin.

Rollout requires phased deployment, starting with non-critical assets, and must include robust controls. The orchestrator agent must enforce business rules (e.g., certification requirements for lockout/tagout) and integrate with IAM for system access. A human-in-the-loop approval gate is mandatory for low-confidence assignments or critical assets. Observability is built via a centralized audit log capturing the agent’s decision rationale, all data inputs, and the final dispatch action for compliance and continuous tuning. This ensures the system is both effective and governable at scale.

This workflow automates the high-stakes bottleneck of assigning the right technician to the right job at the right time. Upon work order creation in your CMMS (e.g., SAP PM, IBM Maximo), specialized agents evaluate real-time signals: technician GPS location from mobile apps, certified skills from HR systems, current workload from the dispatch board, and parts availability from inventory. The orchestration logic resolves these constraints to assign the best-suited technician, eliminating manual coordinator guesswork and reducing mean time to repair (MTTR) by 30-50%, directly protecting production throughput.

Implementation integrates with your existing CMMS via API or event stream, deploying the orchestrator as a containerized service. The multi-agent layer handles exception routing—like high-risk lockout/tagout procedures or missing parts—to a human supervisor console. Controls include mandatory digital work instruction acknowledgment, GPS arrival confirmation, and post-repair quality sign-off fed back to the digital twin. This creates a closed-loop, audit-ready maintenance system that scales with production variability.