How to review AI-generated work with your team

To review AI-generated work as a team, agree on the brief, open the same saved version, check its claims against the source material, and name the person who makes the final decision. Keep the review alongside the project so the next person can see what changed and what still needs work.

Think about a proposal. One person has the customer context. Another has the financial model. A third has an AI-generated deck that almost tells the right story. Everybody has a file. Nobody is quite sure which one is the proposal.

Making another draft is easy. Getting the team to agree on the same one is the work.

What teamwork can borrow from GitHub

GitHub gives software teams a shared place to discuss and review changes. That is the idea behind a “GitHub layer for knowledge work”: bring the same care to a proposal, a board update, or a client presentation.

A useful habit in software is treating a change as something people can inspect together. The author matters. The context matters. The version matters. A conversation about an old version should not silently become a conversation about a different one.

Those habits belong outside engineering, too. A pricing recommendation, a board update, and a client presentation all need them.

For TalentOS, this is a design idea rather than literal software version control. The aim is to make shared work understandable enough that another person can pick it up and move it forward.

1. Agree on the brief and the reviewer

In TalentOS, a Room is a shared space for a project, client, or team. People can work there with the agents they already use, including Claude Code and Codex.

For the proposal, create a Room around the actual outcome: “Acme renewal proposal.” Add the people responsible for the customer, the numbers, and the final decision. Give them a short brief:

We need a renewal proposal the account owner can send. Use the agreed scope and current pricing. Separate confirmed facts from open questions. Maya owns the final review.

That is more useful than a room called “AI stuff.” The name tells a teammate why they are there. The brief tells an agent what good work looks like.

Room membership is explicit. Members can read the conversations shared there. Personal chats, personal memory, private runs, and approval decisions remain owner-only. Sharing a project does not mean sharing everyone’s entire AI history.

2. Review the same saved version

A conversation is where a team explores. An artifact is the work someone needs to use afterward.

TalentOS supports saved document versions, alongside deliverables such as decks, spreadsheets, PDFs, and web apps. A saved document can be revised while its earlier versions remain identifiable. Room document references stay pinned to the version that was shared.

That distinction helps a reviewer say something precise: “The scope in this version is correct. The commercial recommendation still needs work.” It also avoids treating every later edit as something the reviewer has already approved.

Sharing is an explicit step. Creating an artifact does not automatically make it visible to the Room or the company. Choose the appropriate audience for the work you intend to share.

3. Check the claims and record the next decision

Before approving a proposal or report, check the claims against the material it was based on. Can you find the source for each important number? Has the draft separated a confirmed customer request from an assumption? Does the recommendation still fit the brief?

Ask the person closest to each subject to review that part. The account owner checks the customer story; the operator checks the commercial assumptions. Record unresolved questions alongside the version under review.

The best handoff is short:

• Here is the saved version to review.
• These are the decisions it reflects.
• This question is still open.
• This person owns the next move.

An agent can help prepare that handoff. It still acts for a person. AI-written Room replies remain attributed to that human owner, and tool actions stay subject to the owner’s approval rules.

The point is to make responsibility easier to see as more of the drafting happens through AI.

Try it on something small

Take one piece of work your team already passes between people. A customer proposal is a good start. So is a weekly operating review.

Create one Room. Name the final reviewer. Share only the necessary context. Produce one useful artifact and link the exact version under discussion. At the end, ask the next person to explain what changed and what they need to do.

If they can do that without a separate briefing, you have improved the workflow.

For a concrete example, follow one project from brief to launch. For the underlying model, start with what multiplayer AI means.