I Needed a Workflow Engine for AI Agents. None of Them Fit. So I Built One.

Part three of the vexdo series — after building a local AI dev pipeline and moving it to the cloud vexdo works. I use it. It handles the boring parts of shipping code — the implement-review-fix loop that used to eat my afternoons. At some point I started thinking: could this be something more than a personal tool? Not just a CLI I run on my machine, but an actual product. Something with a proper foundation, not held together with state files and hardcoded pipeline logic. And that's where things got complicated. The problem with "just use a workflow engine" The obvious answer when you want to orchestrate multi-step processes is: use a workflow engine. Airflow, Temporal, BullMQ, Prefect — there are plenty of them, and some are very good at what they do. The problem is what they're good at. These engines are built around a core assumption: you know your steps upfront . You define a DAG — nodes, edges, dependencies — and the engine executes it. The graph is fixed. That's the contract. For