Target outcome · Improved equipment availability and extended asset life through proactive, data-driven maintenance interventions — reducing unplanned downtime and optimising maintenance resource allocation.
Business problem
Manufacturing operations face a compound failure cycle driven by unpredictable equipment degradation patterns. Equipment failures strike without warning at a cost of $50,000–$250,000 per hour in lost production, while subtle degradation patterns go unnoticed until catastrophic failure occurs. Experienced technicians retiring with decades of pattern-recognition knowledge, combined with increasingly complex equipment generating thousands of data points per minute, creates a knowledge gap that traditional monitoring cannot fill.
What it does
The Equipment Performance Agent is an autonomous Decision Agent that operates within XMPro's APEX AI orchestration layer using Composite AI — combining physics-based models, expert rules, causal reasoning, machine learning, and statistical analysis.
Current process vs. with AI Agent
Outcomes and measurement
Unplanned downtime
Maintenance resource utilisation
Asset service life
Maintenance planning cycle time
*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.
Data inputs
Other
including vibration signals
temperature readings
pressure measurements
electrical parameters
oil analysis results
*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.
Scoping questions
Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.
- Which asset classes are highest priority — rotating equipment, conveyors, compressors, or other types — and what sensor coverage currently exists for each?
- Do you have historical failure records and maintenance logs that can be used to seed the agent's physics-based and machine learning models?
- What is the criticality tier structure for your assets, and how should the agent weight availability versus maintenance cost trade-offs for each tier?
- Which CMMS or EAM system needs to receive condition-based work orders, and what integration mechanism is available?
- What autonomy level is appropriate — advisory alerts only, or bounded autonomous work order creation for low-risk, high-confidence recommendations?
Want our AI to walk you through these scoping questions?
SPEAK WITH OUR TEAM
Get specialist advice on scoping this for your site.
Our specialists will help you understand how the Agentic Equipment Performance Agent (Availability Specialist) fits your operations, what data you'd need, and what a scoping engagement typically looks like.
