Guide · Industrial artificial intelligence

Autonomous AI agents what they are and how they work in a plant

An autonomous AI agent is a program that receives a goal, decides on its own the steps to reach it, uses tools such as reading a sensor, finding a procedure or creating a work order, and checks the result before moving on. In a plant it works within written limits: it reads and proposes, and a person decides.
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How it looks today

The alarm says what happened, not why or what to do

ALARM · LINE 2 PLC · 10:02M-204 motor overloadHas it happened before?14-Jul work order, in the CMMSWhat is failing?M-204 vibration sensorHow is it fixed?bearing procedure §2Is there a spare?stores: 1 left, minimum 3

The answers exist, but in four places: the July work order in the maintenance system, the vibration on a sensor, the procedure in a folder and the stock in the storeroom. Pulling them together depends on who is on shift and how much they remember.

What it is

What an autonomous AI agent is made of

A goal, a model that reasons, tools, memory and limits. Any platform brings the first four; the plant decides the fifth.

Agentinside the AI harnessPerceivesalarm, sensor, messageLooks upwhat the plant knowsProposesan action, with sourcesA person decidesapproves or rejectsIt is recordedwho, when, why

Goal

In the iGromi engine

"Explain why line 2 stopped and what to do."

Model

In the iGromi engine

The Swappable Cognitive Core: local on the Edge or via API.

Tools

In the iGromi engine

Read PLC and sensor signals, find work orders, create a draft.

Memory

In the iGromi engine

The Second Brain: only approved, current knowledge, with its source.

Limits

In the iGromi engine

What it looks up, what it proposes and what it may execute with authorization.

Differences

Autonomous agent, chatbot and classic automation

The PLC and SCADA keep doing what they do well: control and supervise with fixed logic. The agent does not replace them; it works on top, reading their data read-only.

PeopleCarla and Luis: decide and actAI agentreads, cross-checks, proposes with sourcesSCADA / HMIsupervises, displays and alarmsPLCcontrols the machine in its scan cycleM-204 motor · line 2 conveyordata · read-onlyneverwrites

How it starts

Chatbot

With a question

PLC / SCADA

With a fixed rule

Autonomous agent

With a goal

What it does

Chatbot

Answers and waits

PLC / SCADA

Runs the same logic every time

Autonomous agent

Chains steps: looks up, cross-checks, proposes

Where it runs

Chatbot

In a chat

PLC / SCADA

In the PLC, in the machine scan cycle

Autonomous agent

In the engine, outside the control network

What it knows about the plant

Chatbot

What you type

PLC / SCADA

The signals wired to it

Autonomous agent

Signals, work orders, procedures and history

When it is wrong

Chatbot

A bad answer

PLC / SCADA

Fails the same way every time, and it shows

Autonomous agent

A chain of steps built on a bad premise

What it requires

Chatbot

Checking the answer

PLC / SCADA

Testing and validating the program

Autonomous agent

Written limits, sources and a log of every step

On the plant floor

How an agent works in a plant, step by step

The line 2 conveyor stops at 10:02. This is how the agent and the people resolve it in 30 minutes, with the July failure on record.

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The Orchestrator

Look up, propose and execute: the three levels

In the iGromi engine, the Orchestrator coordinates Agents with their tools and permissions. Every action falls into one of three levels, and the third exists only if someone authorized it beforehand.

  1. Level 1

    Look up

    Gets information without changing the operation.

    "How many times has the M-204 failed since July?"

  2. Level 2

    Propose

    Recommends an action without carrying it out.

    Drafts the work order for someone to approve.

  3. Level 3

    Execute

    Carries out an action that was authorized and audited beforehand.

    Creates the order Carla asked for, under her name.

  4. Never

    Write to the PLC or change setpoints on its own. Machine control stays with the PLC and with authorized people.

Risks and controls

What can go wrong with an autonomous agent and how it is controlled

DECISION · ORDER 42 · M-204ProposedOrchestrator · 10:04Sources14-Jul order · sensor · proc. §2DecidedCarla, plant manager · 10:05ExecutedLuis, maintenance · order 42Outcomeline running at 10:32Learnedcriterion saved on the M-204

Every decision leaves its trail: what the agent proposed, from which sources, who decided and what happened next.

It answers with what it believes, not what is on record

Control

It only uses approved, current knowledge and cites the source of every figure.

It chains steps on a wrong premise

Control

It stops at "propose": a person reviews before anything changes.

It touches the machine

Control

It reads the PLC read-only. It does not write setpoints or change the program.

It sees more than it should

Control

Permissions by access level: the agent sees what its user is allowed to see.

Nobody answers for what it did

Control

Every proposal and every approval is logged with who, when and what evidence.

It depends on one AI vendor

Control

The model is swappable: local on the Edge or via API. Memory and permissions stay the same.

Your data

Where the agent works and where the data stays

Free

iGromi cloud, separated per company

The shift app: chat, photo and voice records, and AI that answers with its source.

Premium

A private, isolated cloud for your company

Adds SAP, ERP and spreadsheets. The AI model runs inside that cloud.

Enterprise

Inside your plant, on Edge or on-premise

Adds PLCs, sensors and cameras. The Edge reads the PLC read-only and keeps measuring offline.

FAQ

Autonomous AI agents FAQ

What are autonomous AI agents?
They are artificial intelligence programs that receive a goal, decide the steps to reach it, use tools such as a database or a document and check the result before moving on. In a plant they work within written limits: they read data, look up what the plant knows and propose actions that a person approves.
How is an autonomous agent different from a chatbot?
A chatbot answers a question and waits for the next one. An agent starts from a goal and chains steps on its own: it checks a sensor, finds the previous work order, cross-checks the data and prepares an action. That is why it needs something a chatbot does not: limits on what it can do and a log of every step.
Does an AI agent replace the PLC or SCADA?
No. The PLC controls the machine with fixed, tested logic in its scan cycle; SCADA supervises and alarms. The agent works on top of them, reading their data read-only: it explains why something happened, finds how it was solved before and proposes what to do. It never writes to the PLC.
What is the Orchestrator?
It is the component of the iGromi engine that coordinates Agents, their tools, permissions and workflows. It receives an intent, consults the Second Brain and distinguishes three levels: look up, propose, and execute only what was authorized beforehand and is audited.
What is an AI harness?
It is the software layer that wraps a language model so it can work as an agent: it gives it context, tools, memory, permissions and a control loop. iGromi's industrial AI harness adds what a plant demands: the operation's ontology, deterministic rules, memory with authority and validity dates, and logged human approval.
What are the risks of an autonomous agent in a plant?
Answering with made-up data, chaining steps on a wrong premise, seeing information it should not, or acting on a machine. They are controlled with cited sources, access-level permissions, human approval before any change, and read-only PLC access.
What does a plant need to use AI agents?
Data that arrives on its own (sensors, PLCs, cameras), written knowledge with an owner and a date (procedures, past work orders) and a person who reviews what the agent proposes. The model is the easiest part to get; what is usually missing is everything else.

Keep reading

Agents, industrial AI and the engine

Autonomy levels and OEEFive levels to climb one at a time, with a template to assess your plant.Industrial artificial intelligenceVision, IoT and Edge AI on the equipment the plant already has.Agentic AI in manufacturingMore cases of agents on the plant floor, with examples.What is RAGHow an agent answers with what is on record, not what it believes.The iGromi engineWhere the Orchestrator, Agents and Second Brain live.Secure industrial AIRead-only access, permissions and where your data stays.

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PLANT · SHIFT A
Tasks
Luis · maintenance
OVERDUE
Sugar silo 1
No signal since 05:40
Sensor · supply
TODAY
M-204 vibration
4.1 mm/s, above normal
Detected by the sensor
PROPOSED
Check the capper
14 badly seated lids since 08:30
AcceptDismiss
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