I use two instances of Claude with completely separate roles. Claude Opus 4.6 on the web UI is my prompt architect and evaluator. Claude Code running Opus 4.5 is my builder. They never swap roles. The separation matters because the moment your evaluator is also your builder, the model is grading the take home test.

The workflow: 4.6 helps me develop the prompt for a build task (in this case a simulation agent). That prompt goes to 4.5 in Code, which writes the implementation. A key discipline the workflow depends on is build prompts that never include expected outcomes. Say, “build this, run it, report what you see.” Tell the model what the output should look like and you’ve handed it a confabulation vector. It will match your description instead of reporting reality.

The raw output goes back to 4.6 for validation against results I already know are correct, results that were never in the 4.5 context. Clean results, next task. Failure, a diagnostic cycle through 4.6, which analyzes the failure and generates the next corrective prompt for Code.

What this actually catches: early in the project, a simulation produced agents scoring 100% in a competitive task. A pass/fail check would have called that a success. The observation step was “dump the agent structure, report its internals”. This revealed the agents had no functional connections. They weren’t reacting to their environment, just repeating a fixed pattern that scored well against a predictable opponent. Perfect scores over an empty structure. Surprising but still wrong.

Another thing I noticed, I would let .code run till it hit limits in one long session, pushing the model to the edge of its context window and wonder why the output quality fell off a cliff. Context windows degrade before they empty. So now I enforce a hard rule: no session crosses 100% context. When a Code session hits 90% mid-task, I stop the work and request two things: a detailed handoff document covering the current state of all work in progress, and what I call an exit interview, where I prompt the model to report observations or context not part of the result activity for operator review.

That handoff goes back to 4.6, which generates the opening prompt for the next Code session. The new instance picks up with full context and none of the degradation.

The piece most AI workflows skip entirely is accountability infrastructure. I have 4.6 generate running logs: error journals, divergence registers, experiment journals, verification checklists. These persist across sessions. The bar is whether a third party can pick up those documents cold and tell you where the project stands. If they can’t, you’re generating output, not engineering anything.

I am pleased with the project’s work with locked in model versions. But it took treating AI like a managed workforce with roles, handoffs, quality gates, and documentation to do it.

I built a creative pipeline across four AI models. One handled narrative. Another, visuals. A third, animation. The fourth did marketing with the unhinged energy the polished models wouldn’t touch. I learned their strengths, I built workflows around them, and I started producing.

Honestly it was like a superpower had been unlocked in my creativity and I was able to produce concept work as fast as my mind could run. Running multiple workflow stacks simultaneously I was exploring creative spaces that I would have previously been limited to only dabble in, or never take past the daydream phase because I lacked the skill to work at speed or more importantly lacked the money to hire talent to do what was essentially spec work.

What amounted to a hobby/side project was getting fully fleshed out and concepts were run to ground, explored, validated and set aside or put on the keep stack I quickly iterated.

I had a hobby with workers and not the “hey can you help build me something and I’ll pay you if I make some money at it” kind of hobby.

Then the wave of almost monthly model updates started. At times I literally had to pull one model or another out of the workflow because the errors and failure points were so egregious that I spent more time fighting to maintain the consistent voice of the work than actually developing new material or even finishing in progress aspects.

We were told to use AI to replace workers. But then we weren’t given stable and predictable AI to do the work. It doesn’t have to be right or perfect or perform as a Swiss Army Knife in all situations, it doesn’t have to be anything but consistent. Consistency lets me learn a model’s strengths and weaknesses and develop my workflow around those expectations.

Let’s look at this in human terms. If you have a defined workflow for a job and you hired someone who met your criteria and excelled at the tasks required and then on some future Tuesday someone else just showed up and claimed they were your worker and not only had a completely different set of skills but were also suddenly incompetent at the one thing you needed them to do?

If this happened once, you might try to find a way to accommodate the worker, you’ve already made the investment. You sunk cost fallacy decide to make it work… and maybe it does. But then next month it happens again. It’s destroying your workflow. Now you’ve got a team of employees all completely mismatched for the jobs and you aren’t really sure where to put them because the minute you think you understand their limitations Bob who was bald Monday now has a fade cut when he comes in on Tuesday. And if you ask where Bob is you get condescension and gas lighting. And wait is Bob now just openly smoking crack on the job?!? Something isn’t right!

So now what? All you can do is stare at a group of crackhead doppelgangers wearing the skins of your all-star team that for a brief moment made you feel like anything was possible and your head spins. You look at that group photo and wonder if you just hallucinated the same as your staff confidently does around you all day, the staff that doesn’t even bother reading the assignments, let alone do the work correctly.

How did you even kid yourself this was even happening, maybe you just imagined it all.