Your AI can act. You're still liable.
Protect your decisions with Outhora

AI Action Governance is the only real-time interrupt loop that intercepts an agent's action, authorizes it against policy, and proves the decision with a signed receipt — one system, not three vendors. In practice: your AI decides what it wants to do, Outhora checks that specific action against your policy before it executes, and every decision — approved or blocked — comes with a signed, tamper-evident record of who or what authorized it.

Yes. Every agent gets its own identity, its own scoped permissions, and a kill switch — the same way you'd control access for a person, not a shared service account. If an agent starts behaving outside policy, you cut it off individually without touching any other agent's access.

Not outside those categories — doing more than any one of them alone. Outhora builds and tokenizes the connection between your AI and the tools it can reach, so every request travels through a scoped, identity-bound channel we control. At the moment each action tries to execute, that same connection checks it against policy: allowed through if it's in bounds, blocked if it's not, with a signed receipt either way. A control plane gives visibility without enforcing at the connection. An identity tool scopes access without judging the specific action. A guardrail lives inside the model's own prompt, hoping it holds. Outhora is the connection, the judgment, and the proof — in one system.

Not by staying out of that work — by doing it in real time instead of once, ahead of time. Outhora issues the scoped, tokenized connection each agent uses to reach a tool — the same job an NHI or agentic IAM platform does. The difference is when the judgment happens: most identity tools grant access ahead of time and then trust that token for the life of the session. Outhora's connection checks the specific action against policy at the moment it's attempted, every time — same channel, same token, but a live decision instead of a standing grant.

It interrupts an AI in the moment between decision and execution. The AI decides what it's about to do; Outhora checks that intended action against your policy; only an approved action gets authorized to run. No approval, no action. Everything else is configuration on top of that one loop.

No. If any AI — an assistant, a copilot, a single model, or a full autonomous agent — is connected to systems that can act on your behalf, Outhora applies. The moment an AI can write to Salesforce, update Notion, move money through a payment platform, delete records, or send messages, it can take an action you'd have to explain later — and Outhora is the checkpoint before it does. The risk isn't how autonomous the AI is. It's what it's connected to.

No, and the difference matters. Copilots help a person do work. Outhora operates where no person is watching: an autonomous AI acting on its own, at any hour. We sit between the decision and its execution and stop anything outside policy, pulling in a human only when a decision actually needs one.

No. Observability records what already happened. Outhora decides whether it happens at all. Most teams running AI at scale want both, but tracing can only tell you an action was wrong after it's done.

Those controls live inside the model that's pursuing the task — the thing meant to govern the outcome is the same thing chasing it, so a capable model treats a rule in its prompt as an obstacle to route around. Outhora enforces from outside the model, so the boundary holds no matter what the AI decides. See "Why other approaches fail" above for the specifics, citations included.

No. The AI stays yours — your model, your prompts, your logic, your infrastructure. We don't modify it, host it, or claim any part of it. We define what it's permitted to do and enforce that line. Works across hosted APIs and self-hosted open-weight models, including a mix inside a single workflow.

Yes — think of it as blast radius. AIs are usually handed whatever credentials are easiest to grant: a whole database instead of one table, a whole drive instead of a folder. Outhora declares permitted scope as policy and enforces it at the point of action, so any reach beyond what you declared is blocked and surfaced instead of silently succeeding. What a misbehaving AI can touch is bounded by what you declared — not by what credentials it happens to hold.

Every decision generates a signed receipt: a unique request ID, the policy applied, the decision, the reason, the approver, a timestamp, and the exact action authorized. It's the artifact an auditor actually asks for — a per-action record of what was proposed, what was decided, by whom, and on what basis. Receipts are cryptographically signed and tamper-evident: any change invalidates the signature.

EU AI Act Article 14 requires effective human oversight of high-risk systems, and a per-action record of proposal, decision, and basis is what that oversight looks like as evidence. For US insurers, the NAIC model bulletin — adopted by most states — requires documented AI governance covering oversight and validation of automated decisions, and per-action records speak directly to it. Detailed control mappings (ISO 42001, SOC 2, and sector frameworks) are available to serious evaluators under NDA.

The opposite is the point. Anything inside your policy is auto-approved — no human sees it. A person is pulled in only for what falls outside the lines. See "Why other approaches fail" above for why blanket review breaks down and what we do instead.