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service.ai_agents()

Industrial AI Agents

operating context

When the data is there but nobody uses it

Industrial AI projects often start with a phrase: "we have the data, we want to use AI". The problem is that without a precise operational use case, contextualised data and clear governance, the agent remains a PoC in a notebook.

01

Use case framed as technology, not as output

Issue

"We want a chatbot on documentation" is not a use case. "The operator wants to find the maintenance procedure in 30 seconds instead of 10 minutes" is.

Solution

Without a measurable expected output, any agent response seems acceptable. Value remains unverifiable.

02

Non-contextualised data

Issue

An LLM agent reading raw data from MES or historian without shared semantics produces plausible but often incorrect responses. Industrial context must be explicitly encoded.

Solution

Without a semantic layer that defines machines, shifts, orders and KPIs, the agent cannot distinguish a planned stop from a failure.

03

No governance after go-live

Issue

Who corrects an incorrect agent response? Who updates the knowledge base when a procedure changes? Without clear ownership, the agent silently degrades.

Solution

An agent without a governance playbook quickly becomes unreliable. Operational trust is lost within weeks.

operating method

How we work: 5 phases in sequence

01

Data context analysis

Mapping available sources: MES, historian, ERP, CMMS, technical documentation. Defining high-impact use cases.

Source mapUse caseQuality criteria
02

RAG architecture

Indexing technical documentation, machine manuals, operating procedures and ticket history into a semantically searchable knowledge base.

RAGEmbeddingChunking
03

Multi-step agents

LangGraph orchestration of agents that query production databases, correlate events and generate contextualised output for the operator.

LangGraphTool useMulti-step
04

Stack integration

Each agent is exposed via internal API and connects to MES, SCADA or ERP through existing integration layers. No separate infrastructure.

REST APIMES connectorAudit log
05

Governance and monitoring

Operator feedback playbook, response logging, quality metrics and knowledge base update process.

Feedback loopKB updateDrift check
expected output

What we deliver in an industrial AI agent project

Every engagement delivers a functioning agent in production — not an isolated PoC. Governance is part of the deliverable from day one.

Definition of inputs, expected output, data sources, response quality criteria and acceptance thresholds — before writing code.

tech spec

Technical spec

explorer
▼ architecture/ 2
▼ operations/ 2
report-turno.md
// use_case.shift_report

Automated shift report

scenario: The operator spent 60-90 minutes at shift end collecting data from MES, historian and spreadsheets to compile the report manually.
how_it_works: The agent queries MES and historian, correlates downtime, output quantities and anomalies from the last shift, then composes text following the company template.
output: Structured report ready in 3 minutes, with anomaly section, previous-shift comparison and automatic flag on out-of-threshold values.
MESHistorianReport
// use_case.root_cause

Assisted root cause analysis

scenario: The line stops at 14:32. The operator does not know whether it is a recurring fault, a quality alarm or a maintenance issue.
how_it_works: The agent reads SCADA logs in the time window, correlates them with CMMS history and searches for similar patterns over the last 90 days.
output: Reconstructed event timeline, probable causes ranked by historical frequency and links to related maintenance procedures.
SCADACMMSHistory
// use_case.natural_query

Natural-language data queries

scenario: The production manager wants the weekly OEE per line without opening an IT ticket or waiting for the Monday report.
how_it_works: The agent interprets the question, generates the query on the data warehouse, aggregates data from MES and historian and composes a readable response.
output: Text response with the requested values, comparative table and automatic alert if a value is below threshold — without writing SQL.
MESData warehouseOEE
// use_case.operator_assist

Operator assistance on alarm

scenario: The operator sees an alarm on a machine they rarely handle. The expert maintenance technician is off-shift. The paper manual has 600 pages.
how_it_works: RAG on machine manuals, operating procedures and intervention history: the agent finds paragraphs relevant to the alarm and combines them with similar previous cases.
output: Step-by-step procedure for the specific alarm, list of similar interventions from the last 12 months with outcomes and reference maintenance contact.
RAGManualsCMMS
operations/report-turno.md Markdown
related services

Explore related services

industrial systems Industrial ML Predictive models trained on real, governable process data. industrial systems Data Engineering Engineered, observable, governable industrial data pipelines — the foundation for any reliable AI agent. industrial systems Custom Industrial Software Custom industrial applications integrating AI agents as native modules.
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