AI Project Management: Shipping Software When Agents Write the Code Something strange happened to my job this year. The typing got cheap. An agent can turn a paragraph of intent into a working pull request while I refill my coffee, and yet the projects did not get faster. Not really. More code shipped, more PRs merged, and somehow the same features took roughly the same number of weeks to actually land.
That gap is what this guide is about. When machines write the code, the scarce thing is no longer the writing. It's the specifying, the slicing-up, and the reviewing. Project management stopped being about handing out work and started being about feeding agents instructions clear enough that their output is worth keeping, then catching the parts that aren't.
I've been running this way for a while now: specs before code, work cut into agent-sized pieces, a small fleet on separate worktrees, and a review loop that assumes the machine got something subtly wrong. This is the playbook I wish someone had handed me. It covers spec-driven development, decomposition, acceptance criteria an agent can test against, parallelism without chaos, the review loop, governance for code you didn't write, and the handful of metrics that tell you whether any of it is actually working.
It's written for engineers and tech leads who already use agents to write code and want to run real projects with them, not demos.
_This is a living document and will be updated as the tools and the workflows keep evolving._
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