In plain language
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.
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 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 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
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 IntelliStream 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.
The agent queries a single context graph instead of stitching systems together on every call. Fewer moving parts, fewer wrong turns.
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.
The agent drafts, a human commits. Approval gates sit in front of anything that changes a record, sends a message or moves money.
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.
Go deeper
The reasoning is written down, in the same plain language. Start where your question is.
Why an agent given a model of your operation reasons better, and far more checkably, than one given raw numbers.
Read the documentationProduct Discovery: two weeks to find the tasks agents can take over today, and what each one is worth.
Read moreWhat the data platform stores, how the model works, and how it deploys in your own environment.
Read moreTell us the task you would hand over first. We will tell you honestly whether an agent can do it today, what context it needs, and what building it takes.