AIFactory takes a signed plan and builds it, each task inside its own throwaway Kubernetes Job. The last three weeks were less about new capability and more about a category of bug that is specific to autonomous systems: the builder reporting state that was true of its intention rather than of the world.
A few weeks ago we taught the build executor to run independent subtasks in parallel, across multiple LLM providers at once. That work was about throughput, and it worked. But it left a blind spot: when a build finished, the cost and the timing came back as a single aggregate. Total tokens. Total dollars. Total wall-clock. Useful, but it couldn't answer the question you actually ask after a parallel run: which worker spent that money, on which provider, in which model, and where did the time go?
This session was about closing that gap. The headline is per-worker observability. The theme is that all of it is additive — the parallel build spine didn't change, we just put instruments on it. The same session also carried a GitHub Actions security pass and a god-file decomposition, because they were sitting in the same backlog and they unblock the work above.
The 3.4 line is about seeing and steering a build while it runs — and trimming the overhead when a job is small. Here's what landed.