An agent for one job, run under your approval.

You tell us the job. The agent might draft thought leadership posts in a leader's voice, write newsletters on a recurring schedule, produce case studies from raw source material, or send outreach emails to specific contacts. One agent does one job. You do not build it, configure it, prompt it, or maintain it.

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

We design the agent. That means picking the prompt scaffolding, gathering voice samples from the leader the agent writes for, defining the topic focus, setting the schedule, and writing the policy rules it must follow. We do this once at the start, then again whenever the agent needs tuning.

We also operate the runtime. The agent is not a continuously running process. On its schedule, or when triggered by your team, it wakes up, generates a draft, passes it through governance, stores it for review, and shuts down. Every run is logged from end to end.

When triggered, the agent loads its configuration from the database — leader name, topic focus, voice samples, policy rules. It also reconstructs memory from its own history: every draft it has produced before, every approval and rejection decision, and any topics it has covered recently. This prevents repetition and keeps voice consistent.

The agent generates a draft by calling a large language model with the prompt assembled from its configuration plus its memory. The output is text — not a published post, not a sent email, not yet visible anywhere external.

The draft then passes through a governance layer that checks five things: whether it matches the leader's established voice, whether its factual claims are supported, whether it complies with your policy rules (banned topics, required disclaimers, confidentiality boundaries), whether it leaks sensitive information, and whether it matches the broader brand voice. The agent does not produce a draft that bypasses these checks. Drafts that fail are held back and surfaced to us, not to you.

If the draft passes, it lands in your approval queue with a status of pending review. You approve, edit and approve, or reject. Only after approval does the agent publish to its target — LinkedIn, Medium, an email recipient, wherever the job dictates.

Your team has a single page for approvals, showing drafts waiting for review with the agent that produced them, the leader they are written for, 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 draft produced, every approval decision, every change a platform admin made to your agents, every governance check that fired. Compliance officers 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 UI invites the kinds of mistakes that produce a bad post under a leader's name. The product promise depends on us being on the hook for quality, not your team.

We do not let the agent publish autonomously. There is no setting that bypasses human approval. This is a deliberate choice, not a feature gap, and is required by every enterprise procurement process we have encountered.

We do not pretend the governance layer is foolproof. Voice match and policy compliance are real checks running on every output. Claim verification, confidentiality, and brand consistency are scaffolded in v1 with lighter checks while the governance engine matures. We say what each check actually does, in the governance documentation.