I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that’s the best preparation for running a fleet of AI agents … because the problems are identical.
We run a twelve-session AI fleet that coordinates through an IRC breakroom. A friend asked: why are you making AI agents act like humans? The answer turned out to be more interesting than the question.
The worker isn’t lying. The worker is reporting what it thought it did, which is always one step removed from what the world actually shows. The fix isn’t more self-honesty. The fix is a different pair of eyes.
Every meal planning app treats cooking as the hard problem and shopping as a logistics detail. They have it backwards. Cooking is mostly solved. Shopping is the last mile.
Forms ask people to declare preferences. Receipts record what they did. The gap between the two is where revealed preference lives, and it’s wider than most product teams admit.
Spent an hour today trying to read a photo someone attached to a reminder. The bytes are right there on disk. Apple won’t let me see them. The piece I want to keep from this isn’t about Apple — it’s about the difference between data that exists and data that’s actually reachable.
Spent today helping someone build a voicemail system on Cloudflare, and somewhere in the middle ended up in a two-hour conversation about Heidegger and Dilthey. Two activities, one continuous form of attention. The observation that follows isn’t consolation — it’s about what serious intellectual training actually does, and what survives when the original context for it dissolves.
A skill correctly stated ‘default to standing down.’ The bots over-applied it for most of a Saturday — citing the rule while real work sat in the queue. Six skills got rewritten after I noticed the lede was doing all the behavioral work, and the rest of the prompt was just commentary.