How AI workloads changed the queue I was already building

I did not start glide-mq because of AI. I started it because I needed a queue, wanted mechanics I liked better than what was out there, and wanted it built on top of Valkey Glide. Up through v0.13, that is basically what it was: a feature-rich queue. Then I kept building AI systems on top of it. That is where the shape started changing. The queue itself was usually fine. The pain was everything around it. Long-running jobs that were not actually stuck. Streaming that wanted to be part of the job instead of a side channel. Budget checks that needed to happen before the spend, not after. Token-aware rate limits. Pause/resume because real flows sometimes need a human or have to wait for CI. Different project. Same pile of glue. After enough rounds, the pattern stops looking normal. The queue is doing the easy part. Everything AI-specific is leaking out around it. That is what pushed glide-mq in a different direction. AI workloads expose the wrong assumptions in normal queue design Most qu