AI agents for business shouldn’t mean handing your company to a black box. Command’s agents run on a governed runtime — versioned charters, per-agent budgets, step-level run traces — and every action they want to take arrives as a proposal a human confirms.
Autonomy is earned, not assumed. Every agent starts at propose-and-confirm, and its autonomy dial climbs only as it earns trust on your account — deliberately decoupled from how powerful the model is. A smarter model doesn’t get more rope. A proven agent does.
Autonomous AI agents with human approval, end to end — a background runtime does the work; the decisions stay yours.
You request an agent from the shelf; the Vevang team deploys it white-glove — never auto-provisioned. Its charter names what it may read, what it may propose, its playbook, and its budget.
The run is queued and a background worker daemon drives it — real model reasoning with no request timeout. Safety gates are checked at enqueue and re-checked when the worker claims the run.
The run trace is an append-only timeline: what the agent read, what it reasoned, what it proposed. Each proposed action drills through to the actual artifact — payload, autonomy, decision.
Proposals wait in the escalations desk — money-out loudest, priority-then-age. You answer, confirm, or dismiss on your own session. Nothing applies until a human says so.
Every mechanism below is enforced by the runtime itself — checked before an agent runs, re-checked when the worker claims the run, and written down where you can read it.
An agent's rules are a versioned constitution — changes create a new version, never edit history. Runs pin the exact version they executed under.
Every agent runs inside a budget. A run that would exceed it is refused before it starts — not billed after the fact.
Append-only timelines of every read, reasoning step, and proposal — errors unmissably red, each action drilling to the real artifact.
The eight money-out action classes are platform-locked at the database: an agent may propose, a human must confirm — forever.
And one discipline over all of it: one universal product. Every agent is the same platform configured to your business — never bespoke code written for one client. When config can’t do something, the platform gains a capability that serves everyone. Deeper guarantees — kill switches, isolation, the audit trail — live on the trust page.
Most “AI agents” are a prompt and a loop. The difference between that and something you’d let touch your business is governance.
Agents don’t guess past their charter — they escalate. Every packet an agent raises lands on one decision desk, ordered by priority then age, with anything touching money-out the loudest thing on the screen. You answer, resolve, or dismiss — and confirm proposed actions on your own session, never the agent’s.
That’s the covenant in daily practice: the agents do the work; the judgment calls come to you, with the evidence attached.
Pipeline agent — 3 deals stalled past 14 days. Proposed: follow-up drafts for each, ready to send.
Each agent here was shaped against a real operation before it was offered to anyone else — and shipped as platform capability, not as a private codebase. Inside the product they sit on a shelf you can ask us to turn on, and each one inherits everything the others already know about your business, because they all read the same ontology.
Your mission and values, the areas of the business and the one person accountable for each, the roles people hold, the procedures those roles carry, and the numbers you hold yourself to — kept as structured records rather than a document nobody opens. Ask it anything and it answers from your own words, citing the record the answer came from.
Then, unasked, it tells you where the manual is still incomplete: an area nobody owns, a role nobody holds, a procedure written but never issued, someone holding a role without the certificate it requires, a number you said you wanted and never defined — and which of your people have not yet confirmed reading what you issued.
That’s how every agent on this shelf started — one business needing something the platform couldn’t do yet. We build it as capability, so it works for you and everyone after you.
Tell us what you’d want built →Each agent operates under a charter that names the data it may read and the tools it may propose — sourcing look-alike customers, drafting follow-ups, flagging stalled deals, raising compliance reminders. Everything an agent wants to change arrives as a proposal with the evidence attached; a human confirms it, and only then does the system apply it. Anything in the money-out family is flagged human-confirm at the platform level.
See the governed runtime live on your own questions — watch a run trace unfold, open the escalations desk, and decide if this is how you want AI working inside your business.