What’s Your Workflow?

First, up front - I’m not a professional SWE. I have written TONs of code over my career but I come from the world of physical products, manufacturing, and assembly lines. I’ve developed code to run automation, machine vision, internal tools, etc rather than existing in the world of SWE workflows. I say this to frame the viewpoint from where I’m asking my questions.

A LOT of the complaint posts I see about using AI for development (especially from people new to AI tools) really boils down to a lot of what physical manufacturing actually is - workflows.

I won’t go ad nauseam into the parallels of SW development/production and physical product development/production as the people I’m poking with my questions here will already get it.

To me AI tools are similar to production line workers. Every one of those workers is different - some are artists at what they do, some barely have a pulse. Steve might make everything with the precision of a tool and die machinist, while Mike keeps trying to put the square peg in the round hole. And what if Steve is sick, so we need to bring in Mike’s buddy Andy? So left to their own devices the output would vary wildly between workers - it’s just as indeterminate as AI tools.

As an engineer in the manufacturing world, my job is to create a WORKFLOW that produces a determinate output. Once handed a design, we need to decompose the problem, and then decompose further into individual tasks - WITHOUT micromanaging. Tasks should be clear enough that most workers can execute well and there’s processes that check/correct errors when they happen. Without some level of this in place the industrial age would never have happened!

So with that preamble…

I’m looking for examples of real, production-proven AI software development workflows.

No prompt engineering tips, or “vibe coding”, etc. I’m looking for what is the professional-level gold standard that the industry is converging toward? Or who seems to be doing it best.

As a personal project and skill-builder, I’m building an internal software project with AI assistance (Claude Code, Codex, etc.), and I’ve hit a bottleneck that I suspect many teams are facing - maintaining engineering discipline over weeks of development.

Examples of problems I’ve run into:
- Models re-adjudicating decisions that were already made.
- Forgetting or ignoring architectural constraints.
- Recording important decisions outside the intended system of record.
- Scope creep during implementation.
- Spending more time reviewing and correcting AI than actually making forward progress.

For context, I’m already using:
- Small, well-defined work packages
- Separate planning and implementation phases
- Decision ledgers
- Architecture documentation
- Human review

I’m studying this problem on a daily basis as it’s fascinating but there’s a lot of moving pieces and developments here almost on the daily. I want to know what everyone else is doing so that I’m not reinventing the wheel of a really good wheel already exists.

My questions are:

If your team is successfully shipping software with AI, what does your workflow actually look like?

What artifacts are considered the source of truth (specs, ADRs, tickets, checklists, etc.)?

How do you prevent models from “freelancing” or silently diverging from previously agreed decisions?

What responsibilities belong to the model versus the workflow/tooling?

Are there any open-source repositories, blog posts, conference talks, or papers where a mature workflow is shown in enough detail that someone else could adopt it?

I’m specifically looking for battle-tested examples from teams working on real codebases, not hypothetical architectures.

If you’ve found something that made AI development feel deterministic instead of constantly herding cats, I’d love to see it.