Your agents hold valid credentials, approved scopes, and tool access. None of that says whether the action they're about to take is what a human actually authorized. Audor reconstructs the chain from human objective to agent action, verifies every action against its delegated scope, and produces the audit trail to prove it.
Identity systems prove who an agent is and what it can access. Observability shows what it did. Nothing in that stack answers the governance question: was each action justified by the objective a human approved?
of the Fortune 500 already run active AI agents.
Microsoft Security, Feb 2026of organizations that deployed agents have any way to govern or control them.
Okta, Oct 2025of enterprise applications will embed task-specific agents by end of 2026 — up from under 5% in 2025.
Gartner, Aug 2025Permission is capability — granted by identity, scopes, and tool access. Authority is whether a specific action is justified by the task a human delegated. Every agent incident that matters lives in the gap between the two.
A thin SDK captures authority context inside the agent framework, where delegation actually lives. The verifier — deterministic policy checks composed with calibrated model judgment — classifies every action against the chain.
The human objective and its scope are recorded at run start.
Every hand-off to a sub-agent carries a narrowed task and scope.
Tool calls are observed at the boundary, with runtime provenance.
Each action is checked against the full chain — scope, exclusions, cumulative budget.
Every verdict is explained, cited to chain nodes, versioned, and exportable.
Audor deploys monitor-first: observe and classify without touching execution, then graduate to enforcement — first with a human approving escalations, then fully autonomous with human oversight — as trust is earned. Ambiguity always escalates — a false allow is the failure mode we optimize against.
Couldn't you just send every action to a frontier model and ask "is this authorized?" You could — and you'd get a plausible opinion with no run state, no guarantees, and no evidence. A verdict you can act on, and defend to an auditor, needs what no single model call provides: the reconstructed delegation chain, cumulative accounting across the run, policy floors that model output cannot loosen, and a reproducible, cited, versioned record. Audor is that system. The model inside answers only the residual semantic question — which is also why verification stays fast, cheap, and deployable in your own environment.
Budgets, quotas, and prior denials are cumulative facts of the run. The fifth in-cap refund looks identical to the first — only a ledger knows the budget is gone.
Deterministic policy floors compose monotonically with model judgment: model output can tighten a verdict, never loosen one — and model failure degrades to escalation, never a silent allow.
Every verdict cites the chain nodes it relied on and carries version stamps — the same request under the same versions reproduces the same verdict. An auditor can replay that; a raw model answer, they can't.
Because determinism resolves most of the question, the model sees only the residual: a fraction of frontier judging cost, fast enough for the enforcement path, self-hostable.
Measured on our internal benchmark; the corpus and figures are shared with design partners under our claims discipline, and not quoted publicly before independent second-rating. A public benchmark — AgentAuthorityBench — is on the way.
Illustrative scenarios drawn from our evaluation corpus — source-backed enterprise workflow patterns with recorded provenance and governed labeling. Customer case studies will come from design-partner pilots.
Independent research and standards efforts are converging on the same requirement: runtime controls that bind agent actions to user intent, delegated scope, provenance, and policy. Audor commercializes those primitives — we did not invent the need, and we cite the work that names it.
"It's like you take an insider threat and you just put it in your company and give it all the access it needs."
"You need to give them identities, you need to give them sandboxes, then you need to set policies to govern them."
"CISOs must now secure intent, not just infrastructure."
"Ninety percent of organizations have deployed AI agents, but only about 10% have any way to govern or control them."
We believe autonomous agents are the biggest productivity unlock in a generation — and that enterprises will only let them off the leash when every action can be proven to match the authority a human delegated. That layer doesn't exist yet as a category. We're building it: neutral by design — not an agent platform, not an identity provider, not a gateway. The layer between them.
We're looking for people who see the same future — one where "the agent might do something harmful" stops being a reason to say no, because the risk is measured, bounded, and provable. If that's the world you want to build, we should talk.
Design partners — you run LangGraph/LangChain agent workflows and want authority-chain visibility with zero execution risk (monitor-only). Pilots are free, structured, and shaped around your workflows.
hello@audorai.com