Due to reactive maintenance practices, a large manufacturing enterprise faced frequent equipment breakdowns, rising maintenance costs, and unplanned downtime. They deployed Agent SRE and Agent analyst on a context-first agentic infrastructure to automate predictive maintenance.
Agent SRE monitors IoT sensor data, vibration signals, and machine logs to detect real-time anomalies. An agent analyst leverages historical and live data to forecast equipment degradation and recommend maintenance windows before failures occur.
This approach transformed the maintenance process from reactive to proactive. By integrating with ERP/CMMS systems, preventive tasks were auto-scheduled, spare parts pre-ordered, and maintenance teams notified instantly. The shift reduced downtime by 45%, cut maintenance costs by 30%, and extended equipment life by 25%, enabling higher production efficiency and safety compliance.
The customer, a global manufacturing firm, struggled with legacy maintenance practices that couldn’t meet modern production demands.
Key business problems included:
Unplanned downtime: Equipment breakdowns halted production lines, causing revenue loss.
High repair costs: Reactive repairs often require emergency part replacements and external contractors.
Inefficient maintenance schedules: Time-based servicing led to over-maintenance of healthy machines and under-maintenance critical assets.
Fragmented visibility: Maintenance teams lacked a single view of machine health, resulting in inconsistent quality and missed opportunities.
Business goals:
Improve asset reliability and reduce unplanned downtime.
Lower maintenance overhead by shifting to condition-based servicing.
Extend equipment lifecycle while ensuring compliance and safety standards.
Support Industry 4.0 initiatives with real-time insights and predictive analytics.
Existing solution limitations:
No intelligent anomaly detection — depended on manual inspections.
Lack of forecasting capabilities — unable to predict failures ahead of time.
Poor integration with CMMS/ERP — manual scheduling and ticketing slowed response.
Inability to scale across plants and geographies.
Compliance and business pressures:
Regulatory mandates for safety inspections required accurate reporting.
Customer SLAs demanded higher equipment uptime and on-time product delivery.
Legacy SCADA and monitoring tools lacked AI-driven insights.
Siloed data from different sensors and machines reduced the effectiveness of the analysis.
Maintenance logs were paper-based or fragmented across spreadsheets.
Rule-based monitoring systems generated excessive false positives.
No reusable data models for predictive analytics.
Outdated servers with no support for real-time data streaming.
Disparate sensor protocols across equipment.
Minimal integration between IoT data, ERP, and CMMS systems.
No centralised logs for tracking anomalies, repairs, and outcomes.
Could not handle large volumes of IoT telemetry at enterprise scale.
Inconsistent performance during high-volume data ingestion.
Lack of observability for predictive accuracy and model drift.
No encryption of maintenance logs or sensor data.
Weak access control across global maintenance teams.
No audit trail for compliance verification.
The company implemented Agent SRE and Agent analyst to create an AI-powered predictive maintenance system.
Agent SRE: Monitors real-time sensor data, detects anomalies, and flags abnormal patterns in machine health.
Agent analyst: Forecasts failure probabilities using historical data, operational context, and real-time telemetry to recommend preventive actions.
The agents work together to:
Auto-generate alerts when anomalies are detected.
Forecast the Remaining Useful Life (RUL) of critical assets.
Auto-schedule preventive tasks in the CMMS/ERP.
Summarise health reports for management review.
This multi-agent, event-driven system enabled proactive decision-making, reduced unplanned downtime, and optimised asset utilisation.
|
Industry |
Use Cases |
Value Delivered |
|
Manufacturing (Primary Industry) |
CNC machines, robotics, assembly lines |
Higher OEE, reduced downtime, lower costs |
|
Automotive & Aerospace |
Engines, turbines, production systems |
Safety, reduced MRO costs, and asset longevity |
|
Energy & Utilities |
Wind turbines, transformers, oil & gas rigs |
Uptime, compliance, optimised asset life |
|
Transportation & Logistics |
Fleet vehicles, ships, railways, cargo gear |
Fewer breakdowns, SLA adherence, and efficiency |
|
Healthcare & Life Sciences |
MRI, ventilators, lab equipment |
Reliability, compliance, patient safety |
|
Smart Cities & Infra |
HVAC, elevators, water treatment |
Cost savings, sustainability, reliability |
|
Semiconductors & High-Tech |
Wafer fabs, cleanroom robotics |
Minimised downtime, precision, and yield gains |
Agent SRE→ Real-time monitoring, anomaly detection, and sensor data collection.
Agent analyst→ Predictive analytics, RUL forecasting, and preventive scheduling.
AgentSRE.ai ingests IoT telemetry (temperature, vibration, pressure) via edge gateways.
Flags anomalies like overheating, vibration spikes, or performance drift.
Agentanalyst.ai applies ML models to calculate degradation curves.
Forecasts the optimal maintenance window before predicted failure.
Integrates with CMMS to auto-create work orders.
Orders spare parts automatically through ERP integration.
Notifies technicians with task assignments.
Improved predictive models reduced false positives by 35%.
Feedback loop continuously re-trains ML models for higher accuracy.
Unified IoT, ERP, and CMMS data into a contextual layer.
Historical + real-time data increased forecasting precision.
Automated anomaly → forecast → schedule → execution.
Reduced manual interventions and improved operational visibility.
Business Benefits:
45% reduction in downtime from proactive interventions.
30% decrease in maintenance costs by avoiding emergency repairs.
25% longer equipment life through optimised servicing.
Improved compliance with auditable maintenance logs and reports.
Technical Benefits:
Real-time anomaly detection at scale across multiple plants.
Parallel processing of thousands of IoT data streams.
99.9% uptime of the monitoring system with auto-recovery.
Full encryption and audit logging for SOC 2 compliance.
"Adopting Agentic AI has been a game-changer. We cut downtime, lowered maintenance spends, and extended asset life — all while stepping confidently into Industry 4.0 with smarter, data-driven operations."
Shift from Reactive to Proactive Requires Cultural Change
Moving from firefighting equipment failures to predictive maintenance was as much about mindset as technology. Training and change management were key.
Quality of Data Defines Quality of Predictions
Diverse IoT sensors, inconsistent logs, and legacy systems initially hampered model accuracy. Normalising and contextualising data streams was critical to reliable forecasts.
Legacy Systems are a Bottleneck
Integration with older CMMS and ERP systems required custom APIs and additional effort. Modernising data flows early could have accelerated deployment.
AI Models Need Domain Knowledge
Generic ML models underperformed until tuned with domain-specific failure datasets and feedback from maintenance engineers. Human expertise enriched AI intelligence.
Scalability Must Be Built from Day One
Predictive enterprise-scale maintenance required robust infrastructure, edge processing, and observability tools to avoid performance bottlenecks.
Security and Compliance Can’t Be Afterthoughts
Encryption, RBAC, and audit trails were vital for regulatory compliance and building trust with global maintenance teams.
Continuous Feedback Loops Improve Accuracy
A cycle of anomaly detection, technician feedback, and model retraining helped reduce false positives and improve long-term reliability.
Start with critical assets before scaling plant-wide.
Use Infrastructure as Code (IaC) to standardise deployment.
Implement monitoring (logs, metrics, model drift detection) from day one.
Secure data pipelines with encryption and RBAC policies.
Maintain a feedback loop between maintenance teams and AI outputs.
Expand Coverage Across Assets
Extend predictive maintenance from critical equipment to complete production lines, fleets, utilities, and logistics for end-to-end operational intelligence.
Adopt Digital Twins & Simulations
Build real-time digital twins to simulate stress, wear, and maintenance scenarios. This will enable plant managers to run what-if analyses and optimise strategies.
Embed Trust, Compliance & Governance
Introduce Agent Trust and Agent GRC to ensure explainability, audit readiness, and adherence to global safety and compliance standards.
Deliver Global, Role-Based Dashboards
Provide multilingual dashboards tailored for executives, engineers, and compliance officers, ensuring actionable insights for every stakeholder.
Advance Toward Autonomous & Sustainable Operations
Move from predictive to autonomous, self-healing systems and leverage insights to reduce energy waste, extend asset life, and support ESG initiatives.
The manufacturer successfully transformed its maintenance operations by deploying Agent SRE and Agent analyst. The solution reduced downtime, improved reliability, and ensured compliance while lowering costs and boosting asset longevity. With predictive maintenance powered by agentic AI, the enterprise is positioned for continued efficiency gains and Industry 4.0 leadership.