You define the job. We deliver the agent, end to end.

You tell us the jobs. An agent might resolve support requests and issue the refund, qualify a new lead and book the meeting, chase unpaid invoices until they are collected, coordinate vendors and keep orders on schedule, or prepare a reconciliation. Each agent does one job, done properly, and most businesses run several. You do not build them, configure them, prompt them, or maintain them.

After the agent is deployed, the only thing your team does is review and approve the work. Approval happens inside the platform, with a logged decision per item.

We design the agent in a working session with you. Together we define what it is for, what it should produce, the systems it can touch, the rules it must follow, and the boundaries it cannot cross, all encoded at the configuration level, not written as aspirational policy documents. We do this once at the start, then again whenever the agent needs tuning.

We also operate the runtime. Agents run on DeployCo managed infrastructure, or are deployed into your environment as part of an engagement, to your specification. The agent is not a continuously running process. On its schedule, or when your team triggers it, it wakes up, does the work of its job, passes the result through governance, stores it for review, and stops. Every run is logged from end to end.

When triggered, the agent loads its configuration, its goal, the tools it is allowed to use, and the policy rules it operates under. It also reconstructs context from its own history: the work it has done before and the approval and rejection decisions on that work. This keeps it consistent and stops it from repeating itself.

It then does the work of its job, reading the relevant data, deciding what to do, and preparing the action or response. What it produces is a proposal. Nothing is sent, changed, or executed yet.

The proposal passes through a governance layer that checks it against the rules defined for your business: whether the action is permitted, whether the tools it would use are allowed, and whether it stays inside your policy boundaries. The agent does not produce an action that bypasses these checks. Anything that fails is held back and surfaced to us, not to you.

If it passes, it lands in your approval queue with full context, what triggered the run, what the agent reasoned, and what it would do. You approve, edit and approve, or reject. Only after approval does the agent carry out the action.

Your team has a single page for approvals, showing work waiting for review with the agent that produced it, what it proposes to do, and the governance results. A reviewer can read, edit, approve, or reject. The platform logs the decision with the reviewer’s identity and timestamp.

You also have an audit log of every action across your organization, every run, every tool call, every approval decision, every governance check that fired, and every change an operator made to your agents. Compliance can reconstruct any agent run, months later, in full.

We do not give customers a builder interface. Configuring an agent well takes craft, and treating it as a self-serve tool invites the kinds of mistakes that do real damage when an agent acts under your name. The product promise depends on us being on the hook for quality, not your team.

We do not turn an agent loose on day one. Consequential actions are approval-gated by default. An agent earns more autonomy only through verified results, within limits you set, and a kill switch returns any agent to fully supervised operation instantly. That is what every serious procurement process expects.

We do not pretend the governance layer is foolproof. The gates on each action, which tools are allowed, which policies apply, and what stays inside bounds, are real and run before every action. We say what each gate actually does, in the governance documentation.