Proof, not just a startup
AI agents are gaining authority before enterprises have clear control over what they can do. Rivaro explores the layer that sits before execution — not only after the fact in logs and dashboards.
The control point
Most tooling observes agent behavior after it happens. Rivaro inserts an identity-aware decision point in the path of execution, so policy is applied while the action is still preventable.
The problem
Enterprises are giving agents, copilots, and LLM-based applications the ability to take real actions — touching customer data, money, and production systems. The governance most teams have is policy documents and after-the-fact monitoring. That tells you what happened. It does not decide what is allowed.
The insight
The real risk with agents isn't hallucination — it's unauthorized execution. So the control point has to sit before execution, not only in logs and dashboards after the fact. Permissioning, approval, escalation, and audit become a runtime concern, not a compliance afterthought.
What I built
Who authorized the agent, and what is it actually allowed to do, in this context?
Sidecar enforcement that applies policy in the path of the action, while it's still preventable.
Visibility into prompts, tools, RAG pipelines, and shadow AI usage across the stack.
Sensitive actions route to a human or a rule before they touch production.
Identity-aware guardrails on what data agents can read, move, or expose.
Defensible records: who, what, which policy applied, and what evidence exists.
Why it matters
Who authorized the agent to act?
What could it access?
What action did it actually take?
What policy applied at that moment?
What evidence exists to prove it?
Rivaro exists to make those answers a runtime guarantee — not a hope.
Design partners
I'm talking with security, AI, and platform leaders to pressure-test where runtime control is becoming urgent.