Most maintenance records say which part got swapped, not why it failed. Here a technician verifies each cause before an AI agent can use it.
For reliability engineers and maintenance planners: link repeat failures and bad actors to their causes, and let agents query only facts a person checked.
Google says Gemini 4 Argon can control robots, build physics-calibrated simulation twins from camera feeds or sketches, and practice a task in simulation before doing it for real. It scores 87% on the ERQA physical reasoning benchmark. For automation teams, the useful part is practicing in a digital twin first, though most of the demos are still in simulation.
IACF's server and Claude Code skills turn a design into an importable, validated Apriso .gpm package, checked against 65+ rules before it builds. It is a vendor product, access needs IACF approval, and the demo output shown is a plant manager dashboard.
For operations and IT teams: an MES project spends much of its time turning requirements into screens, views and queries, and this aims an agent at that work. Ask IACF what the approval involves before planning around it.
A hands-on workshop that builds anomaly detection, fault diagnosis, repair planning, scheduling and parts-ordering agents for a tire plant example, using Microsoft Foundry and Azure. The repo dates from January and was updated Oct 13, and it runs on services Microsoft sells.
For reliability and maintenance teams: it maps the path from sensor alert to repair plan to parts order, and the scenario fits most plants with machines and a spare parts store.