🚨 Trap alert: don’t mix workflow automation (n8n et al.) with AI-native SDLC.

At first glance, they look the same. In practice, they create very different ceilings.

To a non-expert ear, it all sounds similar. But the effects aren’t:

one delivers local, operational gains
the other can unlock global multipliers, compressing years into months

My provocation: tools optimised for predictable processes break down when applied to an emergent, ambiguous process like product development. There’s a clear problem–solution mismatch.

Are AI-powered workflows useful? Absolutely. Are they game-changing? Rarely. Calling workflow steps ‘agents’ doesn’t raise the ceiling.

The transformative results we’re seeing come from multiple factors working together — not just “having agents”:

embedding the capability into the SDLC
progressive quality gates + guardrails + CI (agent harness)
orchestrating multiple agents in parallel with human-in-the-loop

Here’s the 3-question litmus test:

Where does the work happen? Repo/PR sandbox OR inside the real dev loop (explore → edit → run → debug → run again)?
What’s the unit of progress? “Ticket → PR ready for review” OR “small diffs → quality gates → merged → shipped”?
What happens when specs are unclear? Does it require a fully specified ticket OR can it explore, propose, validate, and course-correct?

Workflows thrive on predictable inputs → steps → outputs. Product engineering is emergent and ambiguous.

Different problem. Different operating model. Different ceiling.