AI workflow design
Not understanding a workflow that already exists — designing a new one from a blank page, for a problem nobody's mapped out yet.
Following a map versus drawing one
The AI Workflows guides elsewhere on this site teach how to use an already-designed process — content creation, marketing, research, each with its stages already worked out. Designing a workflow is the step before any of that exists: taking a messy, undefined problem and building the actual sequence of steps, handoffs, and checkpoints from nothing. It's an architecture skill, not a usage skill, and the two don't automatically transfer.
Core design principles
One job per step
A step that both researches and writes is two steps pretending to be one — split it.
Explicit handoffs
What exactly does step two need from step one, in what format?
Deliberate checkpoints
Placed where a mistake would actually be expensive, not sprinkled evenly out of habit.
Mapping inputs and outputs before anything else
A workflow is really a chain of transformations — each step takes a specific input and produces a specific output that the next step depends on. Before designing the steps themselves, map that chain out explicitly: what goes in, what comes out, at every stage. Ambiguity here is where designed workflows quietly break in practice — a step assuming a format the previous one never actually promised to deliver.
Deciding where a human belongs in the chain
Not every step needs a checkpoint, and a workflow with one after every single step isn't safer, it's just slower without much added benefit. Place them where an error would be expensive or hard to reverse, and let the low-stakes, high-volume steps run without one — the same logic covered in more depth in Human Oversight in Automated Systems, applied here at the design stage instead of after the fact.
Designing for when a step fails
Every step in a real workflow will eventually produce a bad result — a design that only works when every step succeeds isn't actually a design, it's an optimistic guess. Decide upfront: does a failed step retry, fall back to a simpler approach, or halt the whole chain for a person to look at? Answering that during design is far cheaper than discovering the gap mid-failure.
Testing a design before trusting it
Run it against a case with a known correct outcome first, not a real one where you don't already know what "right" looks like. Then deliberately feed it a messy, edge-case input — the kind real usage will eventually produce — before it ever runs on something that actually matters.
Where designs typically go wrong
Steps doing too much
- One step quietly handling three different jobs
No failure plan
- Designed only for the version where everything works
Untested on edge cases
- Only ever run on the clean example it was designed around
The short version
Designing a workflow is mapping the actual chain of inputs and outputs first, giving each step exactly one job, placing checkpoints where errors would genuinely cost something, and deciding what happens when a step fails — before any of it runs on something real. A design tested only on the clean, expected case isn't finished; it's untested. For the deeper logic of where automation should stop and a person should take over, see Human Oversight in Automated Systems.