One foot in the AI-forward world, one foot in the before times. Most engineering organizations are stuck somewhere between the two, and getting them unstuck is the job.
A specific problem with an outcome we agree on before it starts: a review process that is not holding up, a stalled adoption effort, an assessment before a decision gets made.
Advisory retainerStanding time each month for the work that does not end: sitting with leadership as decisions come up, coaching managers, keeping a change from unwinding after the first quarter.
Managing AI coding agents, and agents that direct other agents, is structurally the same job as being an engineering manager, a director, a VP, or a CEO. You give high-level direction to autonomous workers, and you operate through systems and outcomes instead of inspecting every artifact.
Horizontal code agents are running on VC-subsidized tokens. Vertical agents are paying sticker price. If you're building one of the latter, you have a problem to solve, and "we have a better prompt" isn't it.
Companies picking an AI plan almost always frame it as a budget question. Which tier gives us the most usage for the money? It's the wrong question.
Every agent company hires from the same labor pool. You and your competitor employ literally the same workers: the same frontier models, refreshed quarterly by the same vendors. Raw capability is identical by construction.
We still assign a human reviewer to every pull request. The human opens the diff, scrolls, approves. That ritual is already dead. Most teams are just propping up the corpse.
I have spent my career inside engineering organizations rather than around them, which means I show up as a working operator, not a slide deck. I work with organizations that know they are behind on AI and are not sure what to do about it, and I start from where they actually are rather than where the hype cycle says they should be.