The approval gate for AI agents

Agents act.People decide.

Noset holds your AI agents’ risky actions in production until the right people approve them — and keeps proof of every decision.

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Free plan, no card. In Noset Cloud or on your own server.

Held at the gate Delete customer Felix Armand (#48213) Pending approval: 2 people from Customer Success

See it work

The request above, in the real product.

HeldThe agent’s DELETE waits, with an AI summary of why it asks.

ApprovedSofia and Lukas from Customer Success say yes: 2 of 2.

RecordedEach step lands in the audit log, signed and hash-chained.

How it works

Risky actions wait for a yes.

01
Your agent asks.

When an agent tries something risky — deleting a customer, moving money, changing infrastructure — Noset holds it. Everything else goes straight through.
02
The right people approve.

The people you choose get the request in Slack, Microsoft Teams or the browser. It goes ahead once enough of them say yes.
03
Every step is on record.

Who asked, who approved and what ran is kept as signed evidence you can check at any time.

What you get

Agents stay fast. People stay in charge.

Routine work goes straight through. Only the actions you choose wait for a person.

Agents work with placeholder credentials.

The real API keys stay inside Noset. A request only gets the real key once your rules allow it or the required people have approved it, so a leaked prompt or log contains nothing an attacker can use.

Proof for every decision.

Every decision is stored as a signed record of who asked, what was requested, who approved or rejected it and what ran. The records are chained by hash, so any later change to the log can be detected. When auditors ask who decided what, you can show them the record. It supports the evidence that frameworks such as NIS2, DORA and the EU AI Act ask for.

One audit log record: Lukas Brandt approved the deletion of customer 48213, 2 of 2, with its time and the verified hash chain that links it to the record before.

Who it’s for

For teams whose AI agents work on real systems, such as code, infrastructure or customer data.

Works with Claude Code, Codex and other command-line agents.

Approvals run directly in Slack, Microsoft Teams or the browser.

Rules are independent of the model. They grow with the organization, from the first agent in a startup to central rule management in a large company.

Available as a cloud version or self-hosted.

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