Why you can trust it
Human-in-the-loop review: agents propose, you approve.
The fear is reasonable: let agents write to the company's knowledge and one bad day rewrites your brand voice. Human-in-the-loop review is how you get the work without the fear — agents contribute all day, and none of it becomes company truth until a person says yes.
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Nothing is silently rewritten
When an agent wants to change something — update a fact, refine a playbook, record what it learned — the change doesn't apply. It's staged as a proposal, away from the trusted library, waiting for review. Your agents can draft against the whole workspace and your library never moves until you move it.
Review is a skim, not a rewrite
Each proposal is a side-by-side of exactly what changed, with the author on it — a teammate by name, or an agent by name — and the source that prompted it. You approve, or send it back with a reason that stays attached. Proposals arrive batched, so a week of agent work is one sitting, not a drip. Teams that spent an hour editing every AI draft spend minutes deciding instead.
One inbox for everything
Everything waiting on a human decision lands in one queue: agent proposals, teammate edits, flagged conflicts, open decisions. And not just the work you asked for — the AI memory feeds the same queue with what agents learned and what's gone stale. All of it arrives the same way: as a proposal, in the inbox, waiting for your yes. Nothing goes straight in.
Approved is a real state
Approved knowledge lives in a dedicated production state with its own history — not a folder named "final". Agents read only what your team ratified. And if review stalls for a week, nothing rots: agents keep working from the last approved version while proposals wait their turn.
Humans in the loop, wherever they are
Review isn't a solo job. Teammates work on the same documents with live presence, leave anchored comments with @-mentions — of people or agents — and resolve threads on the record. Roles and teams decide who can approve what. And every agent works under its own named identity, so "who proposed this" always has an answer.
Go deeper
- Agent identities — named actors with their own expiring API keys, never a shared login.
- The audit trail — the connected record every approval joins: output, knowledge, approval, decision.
Every yes leaves a trail
Each approval stays on the record, linked to what it let in — which is what makes the whole library auditable. That's the audit trail.
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