Looker, part of Google Cloud, is built around one idea: define a metric once in LookML and everyone gets the same answer. Data teams in manufacturing, logistics and utilities use it to model maintenance data from the CMMS and ERP into Explores that the business can query, with governed definitions for MTBF, MTTR, backlog and cost per asset.
That governance is exactly what an AI agent needs. An agent that writes its own SQL can invent its own MTTR. An agent connected to Looker through Agentic CMMS asks Looker instead, so every answer uses the definitions your data team signed off. Your planners get self-service answers, and your data team stops fielding the same requests.
What your AI agent can do with Looker
- Ask a governed question. "What was mean time between failures for centrifugal pumps last quarter?" The agent queries the maintenance Explore with the MTBF measure your team defined.
- Break down backlog. "Show the maintenance backlog in hours by craft and priority." It selects the fields and pivots for you.
- Run a saved Look. "Run the weekly PM compliance Look for the northern region." You get the results with the filter applied.
- Explain a field. "What does 'reactive work share' include?" The agent reads the LookML description and explains it.
- Compare plants. "Rank our plants by maintenance cost per operating hour this year."
- Find content. "Which dashboards use the work order Explore?" Useful when you need to change a definition and want to know what it affects.
- Spot outliers. "Which assets had more than three breakdowns in the last 90 days?" The agent filters on failure counts and lists them with their site.
Looker with your other systems
- When Looker shows an asset with repeat failures, ask the agent to open its work order history in HxGN EAM.
- Go below the model to the raw tables in Google BigQuery when a question needs a field that is not yet in LookML.
- Compare maintenance incidents in ServiceNow with the downtime figures in Looker for the same period.
- Post a daily backlog figure to a Google Chat space for the planning team.
Governed answers, plain questions
The gap between a well-built Looker model and the people who need it is usually skill, not data. Planners do not want to learn Explores; they want to ask about the pumps. The agent closes that gap without weakening the model: it can only use the fields and measures Looker exposes, and it always shows what it queried.
Each person approves their own agent with their own login. Owners and admins decide which roles may use each Looker tool, and every query lands in the activity log for review.