I spent a while optimizing what my AI agents cost to run.
Cheap models for routine work. Better models when the task actually needed them. That made it practical to run several agents without sending every request to the most expensive model available.
It worked. Then I hit the next bottleneck: trusting what came back.
An agent can say a task is done, show passing tests, and leave a clean-looking diff. None of that tells me whether it understood the product decision, changed the right files, or missed an assumption elsewhere in the codebase.
More agent capacity did not remove that problem. It helped me create it faster.
