We are building
the control layer for AI agents.

Everyone is piloting AI agents. Almost no one is shipping them. 79 percent of enterprises have adopted agents, yet only 11 percent trust them enough to run in production, because deploying one means accepting an error made at machine speed and discovered only in retrospect.

Smarter models will never close this gap. They are probabilistic, and real work is a long chain of steps. Errors compound across every one of them, and the tasks enterprises actually want automated leave no room for a miss.

The market's answer has been observability. Billions of dollars into tools that record what an agent did and score how well it did it, after the fact. When an agent is mid-execution and starting to go wrong, that stack offers a rearview mirror of increasing resolution and no steering input of any kind.

A control layer sits in the action path, not beside it. Consequential actions pause at a gate and route to an accountable human, who approves, modifies, or denies them before they execute. Each intervention becomes deterministic guards, fine-tuning data, and regression tests, and every decision writes itself into an audit trail that regulators and procurement now demand.

Sensing without the capacity to intervene is half a system. We are building the other half.

Gartner expects 40 percent of agentic AI projects to be canceled by 2027, explicitly citing weak risk controls. And for the first time, regulation is making ungoverned agents a legal liability. The EU AI Act requires that high-risk AI systems be built for human oversight, down to the ability to intervene or interrupt, and that every decision be logged in a form that can be reconstructed on demand. State laws and sector regulators keep converging on the same three demands: intervene, decide accountably, keep the record.

Procurement got there first. Every enterprise AI questionnaire now asks the same question: what happens when your AI is wrong? A governance policy describes intentions. Reva produces evidence, which actions were gated, who decided, what changed, and how fast, written into an audit trail as it happens.

Governance platforms manage the paperwork. Reva is the operational layer underneath it, oversight that demonstrably changes outcomes instead of documenting them after the fact. Compliance falls out of the architecture, not the other way around.