Use case · Data science
Reinforcement-learning agents on your data, governed before they act
Agents trained against your own reward signals, with the controls an enterprise requires before anything deploys.
The problem
Data science teams are asked for reinforcement-learning agents on the company's own data. The hard part is rarely the ambition; it is the platform. Training, governing, and deploying agents safely takes infrastructure most teams do not have and do not want to build.
The outcome
Reinforcement-learning agents trained and governed on your data, with the controls an enterprise requires before deployment.
What it needs
Your interaction data, your reward signals, and the environments the agents act in.
How it works
You define what good looks like through your reward signals. The platform trains agents toward it on your data and validates behavior before deployment, with every step recorded. Nothing reaches production without passing evaluation.
Controls and approvals
Training, evaluation, and approval are explicit and auditable. Agents deploy only after passing your controls, and the record of how they were trained and evaluated stays available.
Product fit
Orama Zetta, the enterprise platform. Agent training and governance sit on the platform's own training and validation machinery, which is why this is a Zetta capability.
Explore Orama ZettaWhere it runs
Orama Zetta is deployed in your own cloud, on-premises, or on dedicated infrastructure, and agents train and run wherever your deployment does.
Where this stands
Agentic RL models are in development. There is no availability date yet and no performance claim to show you, and we will not pretend otherwise. If agents on your own data are on your roadmap, a conversation now shapes what gets built.
Early conversations carry weight here; tell us what you would need agents to do.