Workspace
An isolated tenant with its own repository, agent roster, rule set, budget and audit trail.
Govern the AI agents that do real work.
Today teams either let agents run with no governance, which is unacceptable for regulated or revenue-critical work, or they don't deploy agents at all. OADI is the layer in between: you define the agents, the rules they must obey, and the approval chains their output passes through before anything ships. OADI enforces all of it.
Run
One execution of an agent graph against a workspace's state, fully logged and immutable once complete.
Rule
A policy enforced at plan, execute or review time. Its severity blocks the run, flags it for human review, or logs it.
Every other feature builds on those three.
Dispatch runs and watch them execute step by stepApprove or reject output through human-in-the-loop approval chainsAn emergency stop that halts an entire workspaceContinuous evaluation against reference datasets, with the option to halt shipping when quality regressesRole-based access and an immutable audit trail for every runManaged SaaS, or a local install that keeps data and inference on your own infrastructure
DevFlow is OADI in production.
We run our own software delivery on OADI. These figures are measured on that governed runtime across 182 production releases.
6.6x
PR throughput
99.1%
Deploy reliability
0
Production deployment failures
182
Releases shipped
What is OADI?
OADI (Operational AI Decision Intelligence) is a control plane for AI agents in regulated operations. People define the agents, the rules those agents must obey, and the approval chains their output passes through before anything ships. OADI enforces all of it.
Who is OADI for?
Regulated and revenue-critical teams that want AI agents doing real work but cannot accept ungoverned output, for example where compliance or legal oversight decides whether something can ship.
How does OADI relate to DevFlow?
DevFlow is an output of OADI. It runs software delivery on OADI's governed pipeline and returns the work as tested, review-ready pull requests.
Does OADI require a specific AI model?
No. OADI is model-agnostic, so the same rules and approvals govern agents whichever model runs underneath.
Can OADI run on our own infrastructure?
Yes. OADI runs as managed SaaS or as a local install for teams that need their data and inference to stay on their own infrastructure.