Industrial AI Agents
AI agents working on your plant’s real data: they query, summarise and act on the operation. Each agent operates within a defined scope, with access to connected data and a bounded set of actions the team decides and supervises.
Industrial AI agents: summary
An industrial AI agent works on the plant’s real data and interacts with the team in natural language. Unlike a generic chatbot, which answers from its general training, the agent queries actual signals, production history and alarm logs, and cites where each answer comes from. It can also execute a bounded set of actions the team has defined, such as preparing a shift summary or triggering an existing automatic workflow.
Typical uses: asking for the state of a line in plain language, an end-of-shift summary of stoppages and alarms, reconstructing what happened on a machine during an incident, and alerts delivered with context alongside prediction and alerts.
Limits are explicit. The agent never writes to process control: no setpoint changes, no starting or stopping machines. Actions are defined in advance, operations with real impact require human confirmation, and every query and action is logged. When a question falls outside its data or scope, the agent is designed to say so instead of improvising, and that behaviour is tested during rollout.
Projects start from data that is already connected (connectivity itself is Connect’s work) and from a small, frequent task such as the shift summary. From there, sources, allowed actions and human checkpoints are defined with the team, as part of Captia’s industrial AI portfolio.
How it connects to the system
Captia AI is a capability of Captia Technology. This solution relies on data Connect integrates and priorities Consulting defines.
Key concepts
Other solutions
- Operational Dashboards
Real-time visualisation of the operational data that matters, designed for decision-making, not contemplation. We build dashboards on top of connected plant data, with the views each role needs to run day-to-day operations.
- Intelligent Reporting
Automatic reports that synthesise what is relevant in the operation without manual intervention. Connected plant data becomes periodic, comparable reports that are generated and distributed automatically to the people who need them.
- Automatic Workflows
Automatic workflows that execute actions when defined conditions are met in the operation. Each workflow is designed on real plant data, chaining the notifications, records and tasks that today depend on manual steps.
Frequently asked questions
- What is the difference between an industrial AI agent and a chatbot?
- A generic chatbot answers from the general knowledge it was trained on and knows nothing about your plant. An industrial AI agent is connected to the operation’s real data: when you ask about the state of a line, it queries current signals and records and answers with what is happening now, not with a textbook explanation. It can also execute a bounded set of actions the team has defined in advance.
- Can an AI agent act on the plant by itself?
- Only within the scope defined for it. Each agent operates with a closed set of actions the team decides and supervises, such as raising a notice or preparing a summary. Actions with operational impact require human confirmation. The agent never writes to process control and never makes decisions outside its scope.
- What data does an AI agent need to work?
- It works on the data the plant already has connected: machine signals, line states, production history, alarms and event logs. The more complete that data layer is, the more useful the agent’s answers become. If part of the information is not connected yet, that connectivity phase is handled first as a Connect project.
- What can the plant team ask an agent?
- Anything that lives in the connected data, in natural language: how many stoppages a line has had this morning, which alarms repeated during the night shift, how production is tracking against plan, or what happened on a specific machine over the weekend. The agent finds the information, summarises it and cites where it comes from.