RESOURCES · AI AGENTS & KNOWLEDGE GRAPH
Agentic AI is reaching the factory floor. The architecture is what matters.
A maintenance question rarely lives in one system. The difference between a chatbot that paraphrases one database and an answer that spans your whole plant comes down to two design choices: one agent per data source, and a Knowledge Graph that joins them.
WHAT AN AGENT ACTUALLY IS HERE Not one chatbot. One specialist per source.
In maintenance, “AI agent” gets used for everything from a search box to autonomous condition monitoring. AutomAssist means something specific: a dedicated agent on each data source — documents, CMMS, ERP, SCADA/PLC, MES, production historians — each fluent in that source’s shape. The document agent reads schematics; the ERP agent understands fabrication orders; the SCADA agent speaks OPC-UA and MQTT. None of them pretends to be the others.
That specialisation is the point. A single model bolted across every system flattens what it doesn’t understand; an electrical schematic, a batch genealogy and a real-time tag are not interchangeable. Specialist agents preserve the domain knowledge a generalist erases.
WHAT THE GRAPH INFERS A Knowledge Graph answers “why,” not just “what.”
Flat database tables answer “what is the value of X.” They cannot express that the bearing that keeps failing is on machines fed by one supplier batch, or that a bad lot traces to a parameter drift and a procedure change at once. A Knowledge Graph encodes those relationships, so an AI agent can follow them across systems — connecting a recurring failure to a supplier batch and flagging the other equipment that shares it.
WHERE THE TIME GOES Root cause in seconds, not a war room.
Traditional root-cause analysis means assembling people and pulling data from SCADA, the CMMS, the ERP and a binder of procedures. The bottleneck is rarely the reasoning — it is the reaching. When the sources are already joined, an agent does the preliminary cross-system trace immediately, and a human validates it instead of starting from zero. The work that took 1 h 52 collapses toward minutes.
HOW TO EVALUATE IT A buyer’s framework for agentic maintenance AI.
- 01Does it span systems, or just one?Ask for a question whose answer requires the CMMS and the ERP (or SCADA) together. A single-source tool can’t.
- 02Is it grounded in your data?Insist on a run against your manuals, work orders and error codes — not a sandbox. Real systems improve as they ingest; “perfect on day one” usually means a rules engine.
- 03Can every answer be traced?Each statement should cite the document or record it came from. No citation, no trust on the floor.
- 04Does it write, or only read?Read-only by default is safer; writes (like raising a work order) should be explicit and audited, not silent.
- 05Who carries the tech-watch?Agentic AI moves faster than an internal team can track. Decide whether you want to chase the field or have it kept current for you.
IN CONTEXT Three ways people frame “AI for maintenance.”
| Agent-per-source + Graph | One monolithic chatbot | Sensor-only condition monitoring | |
|---|---|---|---|
| Best at | Cross-system answers & root cause | Q&A over one corpus | Vibration/temp anomaly alerts |
| Sees documents & work orders | Yes — schematic-aware | Only what it was fed | No |
| Cross-system root cause | Yes — via the graph | No structural join | Only its own signals |
| Needs a sensor rollout | No — uses data you have | No | Yes |
These are complementary, not rivals — but only the graph turns the data you already hold into cross-system answers.
FAQ What technical buyers ask.
- Is this autonomous AI that runs maintenance by itself?
- No. AutomAssist’s agents make your data queryable and trace root cause across systems; they surface answers for people to act on. Writes — like raising a work order — are explicit and audited, never silent.
- What is a Knowledge Graph, concretely?
- A model of the relationships between your assets, work orders, production orders, documents and events — so a question can travel from one system to another. It is what lets a recurring failure be linked to a supplier batch, or a bad lot to a procedure change, across systems.
- Do we need to install sensors?
- No. This is about the data your CMMS, ERP, SCADA, MES and documents already hold. Sensor-based condition monitoring is complementary, but the cross-system answers come from connecting what exists.
- Does every answer cite its source?
- Yes. Each line traces back to the document or record it came from — you can ask to see it. No over-claiming.
- How do we keep up as agentic AI evolves?
- You don’t have to. We carry the tech-watch and keep the stack current, so your team stays focused on the plant rather than chasing the state of the art.
See a cross-system question answered in seconds.
Bring a fault that usually needs three systems and two people — we’ll trace it on connected sources, with every step cited.
Book a demo