Energy & Sustainability
Field Operations Optimization
4 autonomous agents optimize field crew scheduling, routing, and inspection. 30% productivity gain.
Agentic AI Workflow
4 autonomous agents recruit, train, and schedule field crews for utility operations
The Challenge
Field crews spent more time driving between jobs than doing actual work
A utility with 800 field technicians completing 2,500 work orders daily had technicians spending 40% of their day driving between jobs due to poor routing. Manual dispatch assigned jobs by technician availability, not skill match or proximity.
Inspection documentation was paper-based, with data entry taking an additional 45 minutes per technician per day. 15% of inspections required return visits because field data was incomplete. Average work orders completed per technician was 4.2 per day vs. industry benchmark of 6.
The utility needed optimized scheduling, intelligent routing, and digital inspection workflows.
The Solution
Agents that triage work orders, schedule crews, plan routes, and digitize inspections
Vijan.AI deployed 4 agents. The Work Order Intake agent triages service requests by urgency, SLA commitment, and required skills. The Scheduler optimizes crew assignments matching technician certifications, equipment, and current location to each job. The Route Planner minimizes drive time between assignments using GIS and real-time traffic data. The Digital Inspector provides mobile-native inspection workflows with photo capture, GPS verification, and automated completeness checks.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Recruiting
Field Technician Recruiter
Automated Technician Sourcing & Hiring
Posts openings to job boards and screens candidates for certifications and experience, autonomously scheduling interviews and onboarding qualified lineworkers to meet staffing targets.
Input
Staffing forecasts, job descriptions, candidate applications, certification requirements
Output
Job postings, interview schedules, offer letters, onboarding plans
- Calls applicant tracking system to parse resumes and filter by certifications
- Calls job board APIs to post openings and source candidates by region
- Autonomous decision: auto-reject unqualified, schedule interviews, or fast-track experienced hires
- Routes new hires to Safety Planner for initial OSHA and field training
Step 2 · Safety
Workforce Safety Planner
Crew Safety Training & Incident Prevention
Schedules safety training, tracks incident rates, and issues jobsite safety plans, autonomously grounding crews after recordable injuries and escalating repeat violations.
Input
Training schedules, incident reports, OSHA logs, crew assignments
Output
Safety plans, training completions, incident alerts, corrective actions
- Calls incident database to identify crews with recent safety violations
- Calls OSHA API to validate recordability and reporting compliance
- Autonomous decision: ground crew pending retraining, issue PPE, or escalate to safety director
- Routes crew availability to Maintenance Scheduler for work assignments
Step 3 · Maintenance
Maintenance Scheduler
Crew Dispatch & Work Order Management
Assigns preventive and corrective maintenance work orders to field crews based on location, skill, and asset priority, autonomously balancing workload and minimizing travel time.
Input
Work orders, crew skills, truck locations, asset priorities, weather
Output
Crew assignments, dispatch schedules, travel routes, completion ETAs
- Calls CMMS to retrieve work orders by asset type, priority, and SLA
- Calls crew dispatch system to match tasks with certified technicians and truck proximity
- Autonomous decision: batch nearby jobs, defer low-priority work, or call overtime
- Routes crew assignments to Asset Health Monitor for completion verification
Step 4 · Asset Health
Asset Health Monitor
Post-Maintenance Asset Verification
Validates work order completions using SCADA and sensor data, autonomously closing tickets, flagging incomplete repairs, or escalating assets requiring follow-up maintenance.
Input
Completed work orders, SCADA post-work data, sensor readings, crew notes
Output
Work order closures, follow-up tickets, asset health updates
- Calls SCADA to verify asset returned to service and within operating parameters
- Calls sensor network to confirm temperature, voltage, and load readings normalized
- Autonomous decision: close work order, flag for re-inspection, or escalate to engineering
- Routes completion feedback to Recruiter and Safety Planner for crew performance tracking
Results
Measurable impact within 90 days of deployment
Productivity Gain
Work orders per technician increased from 4.2 to 5.5 per day. Equivalent to adding 200 technicians.
Less Drive Time
Average driving time per technician reduced from 3.2 hours to 2.1 hours daily through optimized routing.
Return Visit Rate
Return visits reduced from 15% to 3% through digital inspection completeness checks.
Annual Savings
Savings from improved productivity, reduced fuel costs, and elimination of paper-based processes.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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