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Maintenance agents for manufacturing

A maintenance agent decides when to raise a work-order and for which asset, using live machine telemetry so downtime is prevented rather than reacted to. It runs inside a policy set your reliability team owns, and every action it takes is logged against the asset’s history for audit.

The decision this agent takes

For each critical asset, the agent watches vibration, current, temperature and cycle-count signals. When a signal crosses a learned threshold and the failure-mode probability exceeds the policy value your team sets, the agent decides: raise a maintenance work-order in the CMMS, tag it against the specific asset, and propose a slot in the next planned-maintenance window that does not clash with a scheduled production run.

Data the agent needs

  • Machine telemetry over OPC UA, Modbus, MQTT or PROFINET (vibration, current, temperature, cycle count, alarm codes).
  • Historical downtime and repair logs from the CMMS.
  • Production schedule from the MES for planned-maintenance-window alignment.
  • Parts availability from the ERP so the agent does not raise a work-order it cannot execute.
  • A named failure-mode catalogue per asset class, owned by the reliability engineer.

What it does unsupervised

  • Raise a work-order in the CMMS with asset ID, failure-mode hypothesis, and target execution window.
  • Notify the assigned technician via email or the MES notification bus.
  • Reserve spare-parts inventory in the ERP against the work-order.
  • Update its own confidence score using the outcome (was the predicted failure real?).

Where a human stays in the loop

  • Every threshold change to the failure-mode policy is signed off by the reliability engineer, not the agent.
  • Work-orders above a cost or downtime threshold you set are routed for human approval before execution.
  • The agent’s learned thresholds are reviewed monthly by the plant reliability committee.
  • Emergency shutdowns are always human-triggered. The agent recommends, it does not stop lines.

Where this agent gets it wrong

Honest failure modes and the data-quality prerequisites we insist on before an agent goes live. If your plant does not meet these, the agent runs in advisory-only mode until it does.

  • Sensor drift on a single machine will produce false positives until re-calibrated. Data-quality prereq: quarterly sensor-calibration log per asset.
  • A new failure mode never seen in history will not be detected until it is added to the failure-mode catalogue.
  • If CMMS work-order feedback (was it a real failure? was the repair effective?) is not entered by technicians, the agent stops learning and starts under-performing within 6-8 weeks.
  • If the production schedule is not published to the agent, work-orders will clash with running production and be manually deferred, hurting adoption.

How Ajinkya ships this today

Ajinkya delivers this today via the MES + IIoT stack that already runs on OPC UA and Modbus at reference plants. The maintenance-agent decision layer described above is scoped as a plant-specific pilot; a productionised out-of-the-box agent is [NOT YET SHIPPED] and part of the roadmap.

FAQ

How long before the agent starts making useful decisions?

Typically 8 to 12 weeks. The first 4-6 weeks are pure data collection while the agent learns baseline signatures. Useful decisions start once the failure-mode catalogue has at least 3 recorded instances per mode per asset.

Can it run without a CMMS?

It can raise notifications, but a CMMS is required for the feedback loop that keeps predictions accurate. Without one, the agent goes stale within a quarter.

Does the agent take machines offline?

No. It raises work-orders and reserves spares. Actual line stops stay with the plant operations team.

What is the pilot scope?

A single line or 8-12 critical assets. Longer scope makes the pilot harder to attribute; larger scope inflates the data-collection window.

Talk to the engineers who ship this

A 30-minute discovery call gets you a scoped answer on whether this agent fits your plant, what data your team would need to prepare, and what a pilot would look like.

Book a 30-min call

Parent

See how this agent sits inside Ajinkya’s Manufacturing AI Agents platform →

Reviewed by Amey Kadle, Founder, Ajinkya Technologies. Last reviewed: 2026-08-29.

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