Build your own agents. On data you can already defend.
Agent Studio builds conversational and autonomous agents against the governed model itself, not an export of it. They resolve names through the catalogue, answer inside the permissions of the person asking, and record what they did in the same audit trail as everything else.
Two kinds of agent, one place to build them.
The difference is not the model. It is whether the agent waits for a person, and what it is allowed to do when it does not.
Waits to be asked
Someone asks a question in their own words and gets an answer drawn from live metadata, not a trained snapshot. The agent resolves names through the catalogue, so "revenue" means what the catalogue says it means.
Acts without being asked
A trigger fires, the agent reasons over the same governed model, and it takes the action it was authorised to take. What it may change, and what it must ask about first, is configuration rather than prompt text.
The grounding is the platform itself.
A general assistant pointed at a data platform gives confident answers about tables that do not exist. An agent built here inherits the catalogue, the permission model and the audit trail it was built on.
Grounded on the catalogue
Agents read the same certified definitions the dashboards use. A question about orders resolves through the same business object a report resolves through, so two answers to the same question agree.
Inside the asker’s permissions
An agent answers within the permission model of the person asking, not a service account. Someone who cannot see a column in a report cannot get it out of an agent either.
On your own models
Self-hosted models run on your own infrastructure alongside the rest of the platform, so an air-gapped deployment stays air-gapped when you add agents to it.
Every action recorded
What an agent read, what it decided and what it changed lands in the same chained audit trail as every other action on the platform. There is no separate agent log to reconcile.
Running an agent is the part nobody shows you.
Building one is a demo. Keeping a fleet of them correct, scoped and affordable after six months of prompt changes is the work.
Telemetry per agent
Invocations, latency, failure modes and cost, per agent and per version, so an agent that quietly degraded is visible before someone complains.
Prompt release management
Prompts are versioned and released like code, with the previous version still available. A change to how an agent reasons is a deployment with a rollback, not an edit in a text box.
Scoped by construction
Each agent is scoped to the part of the platform it serves. Scope is set when the agent is built, so a new prompt cannot widen what it is allowed to reach.
Works with the rest of the platform.
Take a model from data preparation to production in one place. AutoML and experiment tracking, one-click deployment as a versioned API, drift monitoring after go-live.
Read moreForecast demand, capacity, or cash with 25+ time-series algorithms. Scheduled runs, accuracy dashboards, and side-by-side backtests, so you can see which model to trust.
Read moreCatch the problem while it is still small. Continuous learning on live SQL, Kafka, webhook, and API streams, with alerts that carry the data that triggered them.
Read moreBuild one against your own catalogue.
Thirty minutes, your connectors, your definitions. Build an agent live and watch it answer inside your own permission model.