First public testimonials, training underway: where TEAF stands today

Something new has been happening with TEAF over the past few weeks, and I'd rather say it directly than wait for a bigger, more formal recap: the first public testimonials about this side project are starting to appear. I won't call them proof here — more on that below, and the distinction matters — but after a year spent defending a deliberately anonymized track record, seeing feedback expressed publicly changes something in how I approach what comes next. Quite simply, I'm proud of it.

What's actually moving: training and certification

In parallel, several efforts are progressing to give the five roles TEAF defines — Architecture Owner, Decision Steward, AI Control Officer, Capability Owner, Compliance Liaison — a sturdier framework. Training paths and certifications are being finalized, with the goal that these roles stop being theoretical definitions in a book and become verifiable, assessable, transferable skills. I'm deliberately not sharing a date or detailed content at this stage: I'll announce a certification once its content is ready to be rigorously assessed, not before — the same reason I never announce a date for the method's own scientific validation.

Same advice as always: go through the official repo

Faced with this early momentum, one caution stays unchanged, and I'll repeat it deliberately: the recommended starting point for experimenting with TEAF remains the official TEAF Light GitLab repo — not an end-to-end reconstruction generated by an AI assistant from the books' PDFs alone, with no human oversight. I recently detailed on this site a scenario illustrating exactly that risk: a fully AI-driven rollout can technically close the TEAF loop successfully, while producing unacceptable drift the moment no human validates structural decisions anymore. That isn't a theoretical caution on my part — it's exactly the kind of approach I advise against, however impressive the results look on paper. An approach too heavily assisted by AI, without the five roles held by identified humans, isn't a shortcut to TEAF — it's a way of bypassing what TEAF is supposed to guarantee.

What's still missing, and where it's genuinely progressing

The most important point remains the one I already made in the article on what TEAF has proven in the field: field feedback, even backed by public testimonials, is not scientific validation, and I won't let anyone believe otherwise. What's genuinely changing, though, is how dense the available metrics have become — see the article on calculating the IVG and IDA in practice for the detail of these two indicators. Data gathered across recent rollouts is more concrete and more systematic than it was a few months ago — less anecdotal, more comparable from one context to another. It still isn't the reproducible, published measurement protocol TEAF needs to be credible beyond qualitative field experience, but it's a real step in that direction, not one more statement of intent.

Why I'd rather say this as it happens

I could wait until there's a complete protocol, published training and more testimonials before revisiting this. I'd rather document progress as it happens, under the same rule that applies to everything else on this site: say what's moving without presenting it as settled, and say what's missing without downplaying it.

• First public testimonials on TEAF: a real development, and one I'm proud of — but field feedback stays field feedback, not proof.
• Training and certification for the five TEAF roles: being finalized, with no date announced before they're ready to be assessed.
• The official TEAF Light GitLab repo remains the recommended starting point for experimentation — not an AI-assisted reconstruction with no human oversight.
• Available metrics are genuinely getting denser (see the IVG and IDA), but the scientific validation protocol remains work in progress, not an already-settled result.