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 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.