Manufacturing
Energy & Sustainability Management
4 autonomous agents monitor consumption, optimize usage, and automate ESG reporting. 18% energy savings.
Agentic AI Workflow
4 autonomous agents drive energy efficiency and carbon reduction
The Challenge
Rising energy costs and ESG reporting requirements demanded better visibility and control
A chemicals manufacturer spending $22M annually on energy had no granular visibility into which equipment or processes consumed the most. Energy bills were reviewed monthly at the facility level, making it impossible to identify waste patterns at the equipment level.
HVAC systems ran at full capacity 24/7 regardless of production schedules. Compressed air leaks wasted an estimated 20% of compressor output. ESG reporting required 2 FTEs spending 3 months annually to manually compile Scope 1, 2, and 3 emissions data from disparate sources.
The manufacturer needed per-equipment energy monitoring, automated optimization, and streamlined emissions reporting.
The Solution
Agents that meter consumption, find waste, optimize schedules, and compile ESG reports
Vijan.AI deployed 4 agents. The Meter Reader ingests sub-metered data from 500+ smart meters covering every major piece of equipment. The Consumption Analyzer identifies waste patterns including idle consumption, peak demand charges, compressed air leaks, and HVAC inefficiencies. The Optimizer agent adjusts HVAC setpoints, machinery startup sequences, and load scheduling to minimize energy consumption while maintaining production requirements. The ESG Reporter automatically compiles Scope 1 (direct emissions), Scope 2 (electricity), and Scope 3 (supply chain) emissions data for regulatory filing and stakeholder reports.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Costing
Cost of Goods Agent
Energy Cost Attribution
Analyzes energy consumption by production line and SKU to calculate energy cost components of COGS, autonomously identifying high-intensity processes and passing findings to carbon auditing in phase 1.
Input
Meter data from production equipment with timestamps and production volumes
Output
SKU-level energy cost allocation with intensity benchmarks
- Calls energy meter API to retrieve kWh consumption by machine, shift, and production batch
- Invokes cost modeling tool to apply time-of-use rates and demand charges to energy usage
- Autonomous decision: flag energy-intensive SKUs, recommend off-peak scheduling, or benchmark against industry standards
- Forwards energy intensity data to Carbon Auditor for scope 2 emissions calculation in analysis phase
Step 2 · Audit
Audit Compliance Agent
Carbon Footprint Auditing
Calculates scope 1, 2, and 3 greenhouse gas emissions using energy data and supply chain inputs, autonomously generating sustainability reports and identifying waste sources for action phase.
Input
Energy consumption data with fuel types and supply chain transportation miles
Output
Carbon footprint reports with emissions by scope and reduction targets
- Queries carbon emissions database to apply conversion factors for electricity grid mix and fuel combustion
- Calls ISO 14001 compliance tool to validate calculation methodology and reporting boundaries
- Autonomous decision: set reduction targets, recommend renewable energy procurement, or offset purchases
- Passes high-emission processes to Waste Detector for detailed efficiency analysis in action phase
Step 3 · Detection
Bottleneck Detector
Energy Waste Identification
Detects energy waste patterns using process maps and thermal imaging, autonomously pinpointing inefficient equipment, compressed air leaks, and HVAC losses before handing off optimization opportunities.
Input
Carbon audit findings with process-level emissions data
Output
Ranked list of energy waste sources with quantified savings potential
- Calls process mapping tool to correlate energy consumption spikes with production activities
- Invokes thermal scanning API to detect heat losses, insulation gaps, and steam leaks
- Autonomous decision: prioritize fixes by ROI, schedule infrared surveys, or flag for capital projects
- Delivers actionable waste reduction projects to Resource Optimizer for implementation in phase 2
Step 4 · Optimization
Resource Optimizer
Energy Resource Optimization
Implements energy-saving initiatives by optimizing HVAC schedules, lighting controls, and equipment operating parameters, autonomously measuring impact and feeding ESG results back to reporting.
Input
Prioritized waste reduction opportunities with implementation plans
Output
Optimized energy consumption with validated savings and sustainability KPIs
- Executes optimizer engine to adjust equipment setpoints, production schedules to off-peak hours, and load shedding strategies
- Calls building management system to automate HVAC, lighting, and compressed air based on occupancy and production needs
- Autonomous decision: implement controls, invest in VFDs or LED retrofits, or procure renewable energy certificates
- Reports verified energy savings and carbon reduction to ESG reporting systems for stakeholder disclosure
Results
Measurable impact within 90 days of deployment
Energy Savings
Annual energy costs reduced from $22M to $18M through equipment-level optimization and waste elimination.
Annual Cost Reduction
Savings from reduced consumption, eliminated peak demand charges, and optimized HVAC scheduling.
Faster ESG Reporting
ESG report compilation reduced from 3 months to 1 week. Data accuracy improved with automated collection.
Carbon Reduction
Scope 1 and 2 emissions reduced 22% through energy optimization, supporting corporate sustainability targets.
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.
Ready to deploy autonomous agents for your use case?
Let's design an agentic AI solution tailored to your organization's workflows.