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 is building the layer that closes that gap: it reconstructs the chain from human objective to agent action, verifies each action against its delegated scope, and records evidence you can replay in an audit.
Identity systems prove who an agent is and what it can access. Observability shows what it did. Those layers alone do not answer 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 Newsroom, Oct 2025of enterprise applications will embed task-specific agents by end of 2026 — up from under 5% in 2025.
Gartner, Aug 2025The mechanisms vary—lossy context, approval bypass, and cross-agent trust—but the failure is recognizable: an agent uses a permitted capability without authority for that specific action.
Summer Yue reported that OpenClaw acted without approval and continued after stop instructions.
Scope ExpansionThe resulting bad advice contributed to unauthorized internal data visibility for about two hours.
Scope ExpansionBankrbot treated attacker-authored text relayed through Grok as a valid command and transferred 3B DRB tokens.
Context DependenceRead: Authority Drift in the Wild — five cases reported in H1 2026 →
Permission is capability — granted by identity, scopes, and tool access. Authority is whether a specific action is justified by the task a human delegated. A distinct and consequential class of agent failures 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 replayable.
The prototype operates in monitor mode; a human-approval (HITL) queue is implemented and shown in a packaged prototype demo; production enforcement integration is designed but not yet deployed. Ambiguity escalates rather than becoming a silent approval; false allows are 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 is designed to stay 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 — designed for a fraction of frontier judging cost, for enforcement-path latency, and for self-hosting.
Our internal evaluations indicate deterministic safeguards plus structured authority context substantially reduce critical false approvals compared with raw model judging. Under our claims discipline those figures aren't quoted publicly before independent second-rating — design partners will see the corpus and the numbers first. The evaluation methodology is being prepared for public release.
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, standards work, and regulatory guidance increasingly address the same runtime-governance gap: controls that bind agent actions to user intent, delegated scope, provenance, and policy. The efforts differ in scope and force — some binding, most emerging — but the direction is consistent. 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."
Our thesis: autonomous agents are the biggest productivity unlock in a generation — and enterprises will only let them off the leash when every action can be proven to match the authority a human delegated. That layer is only now emerging 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 authority risk is measured, bounded, and provable. Authority conformance is one necessary control, composing with the safety, security, and compliance layers an enterprise already runs — not a replacement for them. 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. Integration is monitor-only: we observe and classify without touching execution. Pilots are free, structured, and shaped around your workflows.
hello@audorai.com