I think this was a pretty honest write up of what went well and what didn't, and I think it's directionally pragmatic on takeaways.
> The agent knew the experiment ended at Day 30 since I told it as much in the system instructions, and so it played it safe. It doubled down on what was already working rather than taking creative risks, whereas a (good) human strategist would’ve experimented aggressively in weeks 1-2 and refined later. The agent just tried to ride out the month at a predictable rate.
> Then when I tried to fix quality myself (the email validation gate), it caused the worst performance of the entire experiment. Same trap that human-run campaigns fall into - optimizing for what’s measurable rather than what matters. Main difference is an AI agent just does it faster and with more confidence, which honestly makes it more dangerous.
> If you’re running any kind of recurring workflow where you pull data, make decisions, and act on them, the loop pattern here probably applies to your work already. The hard part is figuring out what to actually optimize for, and clearly articulating that.
AstroBen 2 hours ago [-]
without a human control how do I put these results into context? It doesn't matter that they "hoped" for $2.50/lead
rvz 16 minutes ago [-]
Spoiler: It failed.
> It didn’t go fully as planned, but there were plenty of learnings.
Rendered at 19:57:03 GMT+0000 (Coordinated Universal Time) with Vercel.
> The agent knew the experiment ended at Day 30 since I told it as much in the system instructions, and so it played it safe. It doubled down on what was already working rather than taking creative risks, whereas a (good) human strategist would’ve experimented aggressively in weeks 1-2 and refined later. The agent just tried to ride out the month at a predictable rate.
> Then when I tried to fix quality myself (the email validation gate), it caused the worst performance of the entire experiment. Same trap that human-run campaigns fall into - optimizing for what’s measurable rather than what matters. Main difference is an AI agent just does it faster and with more confidence, which honestly makes it more dangerous.
> If you’re running any kind of recurring workflow where you pull data, make decisions, and act on them, the loop pattern here probably applies to your work already. The hard part is figuring out what to actually optimize for, and clearly articulating that.
> It didn’t go fully as planned, but there were plenty of learnings.