Jeremy Wallace

We're thrilled to announce the integration of our Live Global Leader Board!

After each daily game play, every player is prompted to opt in and add their game to the Global Leader Board if they chose.

A lot of teams start building AI agents before they think about AI governance. That probably makes sense. When you're experimenting, the focus is usually on getting the agent to work, not on documenting every decision it makes.

The challenge is that agents tend to become useful faster than expected. One day they're helping draft content. A few weeks later they're updating records, touching customer data, triggering workflows, or making recommendations that influence real decisions. That's usually when governance stops feeling like a future problem.

If I were putting together a simple checklist before deploying an AI agent, I'd start with a few basic questions. What data can it access? Who can approve its actions? Is there a record of what it did? Can changes be tracked over time? If something goes wrong, can someone reconstruct what happened and why? And if the agent produces a low-confidence result, does the workflow know how to handle it?

None of these controls are particularly complicated on their own. Permissions, approvals, audit logs, monitoring, version history, and failure handling have existed in software for a long time. What's changing is that AI agents are bringing those concerns into places where many teams haven't had to think about them before.

As you may know, we just launched Dirac yesterday!

This is one small step towards building a better email for all of us who are stuck being our own email assistants.

The launch was incredibly fun for me as the Maker of Dirac. Many of you guys signed up for Dirac and our 100 founder members are starting to fill up spots.