AI agents that know your operation, not just your data
In plain language
What an AI agent actually is
An AI agent is software that gets a job done the way a colleague would: it reads the request, looks up what it needs, does the work, and asks when it is unsure. Not a chatbot with opinions, a worker with access.
Every operation has work that repeats: the same form filled in, the same numbers moved between systems, the same question answered for the tenth time this month. That is the work agents take over first.
The catch is the part demos leave out: an agent is only as good as what it can see. Give it half the picture and it answers from half the picture, with full confidence.
It understands the task
You hand it the job the way you would hand it to a colleague, in plain words. No screens to build, no integration project first.
It finds what it needs
It looks up the assets, readings and records the task touches, and it has to find the right ones, not the four copies with different spellings.
It acts, a person commits
It drafts the record, the report or the work order. Anything that changes a system or moves money waits for a person to approve it.
The agent is the part people see. The context underneath decides whether it can be trusted.
The part demos skip
Without context, an agent is a confident guesser
A demo agent answers from whatever it can reach, and in a demo that is enough. In production it has to know which of the four asset lists is the real one, what unit that reading is in, and which pump "P-101" actually names. People carry that knowledge in their heads. An agent has to be handed it.
That is what business context means in practice: your assets, processes, events and the relationships between them, modeled once, kept current, and queryable. With it, the agent reasons about your operation. Without it, it reasons about text.
Where the platform comes in
The context layer, built in, not bolted on
The IntelliStream operational data platform stores your time-series, events and files against one connected model of the operation, and hands that model to agents through an MCP server. We did not build a platform and hope agents would cope; the context layer is the product.
Any agent you run can use it: one you build, one you buy, or the first one we build with you. It queries the same definitions your reports use, so its answers match your numbers, and every answer can show where it came from.
One model, not a dozen integrations
The agent queries a single context graph instead of stitching systems together on every call. Fewer moving parts, fewer wrong turns.
Answers with receipts
Every answer traces back through the model to the raw signals it came from, data-quality flags intact. Your auditor and your engineers see the same trail.
A person stays in charge
The agent drafts, a human commits. Approval gates sit in front of anything that changes a record, sends a message or moves money.
It runs where your data lives
Open source under AGPL-3.0, deployable in your cloud, on your own servers or in closed networks. The context your agents work from never has to leave your environment.
How agents plug in
An MCP server for industrial data
MCP is the open standard AI assistants use to reach the systems around them, and the operational data platform speaks it out of the box. Point the assistant your team already uses at the platform, and it can safely ask questions of your live operational data: which assets ran outside their limits last week, what happened in the hour before the alarm, where a reported figure came from.
It behaves like a careful colleague, not a superuser. The agent works under the same access rules your people have, sees only what the person asking is allowed to see, and every answer keeps its trail back to source. Because MCP is an open standard, you can swap assistant or model provider tomorrow without rebuilding anything.
Go deeper
Read the method, not just the pitch
The reasoning is written down, in the same plain language. Start where your question is.
What an AI agent actually is
Why an agent given a model of your operation reasons better, and far more checkably, than one given raw numbers.
Read the documentationFind the right first agent
Product Discovery: two weeks to find the tasks agents can take over today, and what each one is worth.
Read moreThe platform underneath
What the data platform stores, how the model works, and how it deploys in your own environment.
Read moreFair questions
What teams ask us, answered straight
The questions teams ask before the first agent, answered the way we would across a table.
What can an AI agent actually do with operational data?
Answer questions that used to take a morning: where a number came from, what changed before a failure, which assets behave differently than last month. It drafts the report, the record or the work order from live data. What it does not do is commit changes; anything that alters a system waits for a person.
How do you stop an agent from making things up?
You do not prompt it away, you ground it. Here the agent answers from one governed model of your operation, every answer carries its trail back to the source signals, and when the data is not there, it says so. A wrong answer takes minutes to check, not a meeting.
Does the agent get access to everything?
No. The agent works under the same access rules as the person asking, so it sees what they are allowed to see and nothing more. Approval gates stand in front of anything that changes a record or sends a message. And it runs where your data lives, in your own environment.
What is an MCP server, and why does it matter for industrial data?
MCP is the open standard that lets an AI assistant ask other systems questions. An MCP server for industrial data means the assistant your team already uses can query live operational data, time-series, events, assets and how they relate, without a custom integration and without tying you to one AI vendor.
Do we have to replace our existing systems first?
No. The platform connects to the systems you run today and builds one connected model on top of them. Your historians, registers and logs keep doing their jobs. Agents work from the connected picture; nothing underneath has to move. Most teams start with one task and one agent, not a migration.
Does our data leave our environment?
That is your call, not ours. The platform runs where you put it, including on your own hardware or fully isolated from any network, and an agent reaches your data through the same access rules your people have. Which assistant you connect to it is a separate decision, and it can be one you run yourself when the data is not allowed to travel.
The first agent is the hard one
You do not need a sales call to find out. The platform is open source under AGPL-3.0, so you can read exactly how the context layer works and run it yourself before you talk to anyone. When you have found the task you would hand over first, tell us about it. We will say honestly whether an agent can do it today.