Amy Bradley

Think about what an agent session actually risks. It edits files in a working tree. If that tree was clean when it started, the entire session's damage is one diff you can read and one reset you can throw away. Branches cost almost nothing, so every task can live on its own one, and merging stays a human decision made after reading the evidence.

Underneath that sits the layer most people only meet in a crisis: the reflog. Every commit, reset, rebase, and branch move leaves an entry, kept for 90 days by default even when nothing points at it anymore. For a month or three, almost nothing an agent does to your history with everyday commands is actually gone.

We once had to defeat this on purpose: a clean-room experiment required one commit to be truly unfindable. Deleting it took five deliberate steps remove the remote, delete branches and tags, expire the reflog, garbage-collect, verify. Five steps to lose one commit. That's the strongest endorsement of a safety net we know how to write.

Living in the Google Ads and media buying trenches, I constantly see makers make one of two mistakes: either burning cash on Performance Max campaigns before having true product-market fit, or relying purely on organic hustle for so long that they starve their own growth.

For those of you who have successfully scaled, what was your exact trigger to start buying traffic? Did you wait for a specific revenue milestone, or did you run search campaigns early on just to validate the offer?

Imagine an AI agent responsible for routing inbound leads. Nothing crashes. No alerts fire. The workflow keeps running exactly as expected.

The problem is that the agent has slowly started sending high-value leads to the wrong queue. Maybe a few customer issues are being summarized inaccurately. Maybe records are being updated with small mistakes that seem harmless on their own. Each individual error is easy to miss. Over time, though, the impact starts to compound.

Those are the AI failures that interest me most because they rarely look like failures at first. There is no outage, no red warning message, and no obvious signal that something is wrong. The workflow continues operating, but the quality of the outcomes quietly drifts away from what the team intended.

That makes detection a different challenge altogether. It's less about system uptime and more about observation. Are there feedback loops? Quality checks? Escalation paths? Can someone spot a pattern before customers, revenue, or operations start feeling the effects?