[auslegung]

We use Atlassian and they have helpful tools to query across all our knowledge sources: Slack, GitHub, Figma, Google Drive, of course Jira and Confluence, etc. It is VERY helpful. Doing even more, like you describe, sounds great however I would not want it acting independently. I would prefer “pause the underperforming ads” to result in a plan describing what the LLM would do, and require a human to approve. But this is going to change over time as we get more comfortable with these things taking potential destructive actions. Version controlling everything would be ideal so we can inspect what it did and roll it back if desired

[sameerav]

That’s super helpful, thanks for sharing. We’re hearing the same pattern — analysis is exciting, but acting independently is a trust barrier.

We’ve been experimenting with a “propose → approve → execute” workflow (like you suggested with “pause the underperforming ads”), so the AI drafts the plan, but a human clicks yes before it runs. Kind of like a pull request for actions.

Version control / auditability is a great call — especially if people want to roll back or see exactly what changed. We’ve been thinking about logging each action almost like a Git commit history for ops, so nothing is a black box.

Do you think teams would adopt “propose + approve” mode first, and then maybe move to full autonomy later as confidence grows?

[Hoshang07]

@auslegung - Do you let agents touch internal structured data stored in a warehouse (for example)? If so, how do you do that today? Would love to have your thoughts on this - https://youtu.be/98PZMcYQKDI