A fluent LLM is not a production worker.
A real enterprise task is a chain:
0.95²⁰ ≈ 0.36
That is why demos work, production breaks, and failures escape silently.
So I built agents like a Six Sigma system:
measure first, gate every step, turn failures into permanent controls.
Open source now:
https://t.co/Wa41SF9FwI
An LLM that's fluent in a chat box is not a worker you can put on a production line.
A real enterprise task is not one clever answer. It is a 20-step chain, and reliability is multiplied, not added:
0.95²⁰ ≈ 0.36
That is why the demo works, production breaks, and nobody can explain where the failure escaped.
Before AI, I spent ~13 years doing Six Sigma and process improvement on factory floors. So I built agents the same way we built reliable processes:
measure first,
gate every step,
separate execution from judgment,
and turn every real failure into a permanent control.
The result is open source now: a 7-skill suite for Claude Code, MIT licensed, distilled from my book.
One orchestrator runs the full pipeline:
assessment → control plane → guardrails → human review → independent measurement → DMAIC → production gate
https://t.co/Wa41SF9FwI
I've always said that philosophy is the ultimate major for the AI era.
In an age overflowing with answers, lacking the training to verify truth leads us to nihilism, bigotry, blind conformity, or cognitive atrophy.
But then again, tech giants are now aggressively poaching philosophy talents to train their models... so I guess our own ability to think deeply doesn't really matter anymore.
Totally feel this. Right now, most LLMs behave like that one insecure corporate yes-man who just wants to agree with the boss to avoid trouble. "You're totally right, what was I thinking!"A good human teammate has skin in the game; AI just wants to close the token ticket. I’ve found that telling the AI: "Your job is to be a critical partner, not a yes-man. Push back if I am wrong" helps a bit, but they still lack that genuine intellectual backbone
The key is authority never propagates along the chain — each stage gets a minimal scope granted at a gateway, not inherited from upstream, so a failed stage can't hand down permissions it never held. Add the lethal-trifecta split (no agent holds private data + untrusted input + outbound action at once) and fail-closed gates keyed to reversibility rather than confidence, and a single failure can't assemble an abuse path. It's a containment axis, orthogonal to the pⁿ reliability one.
An LLM that's fluent in a chat box is not a worker you can put on a production line.
A real enterprise task is not one clever answer. It is a 20-step chain, and reliability is multiplied, not added:
0.95²⁰ ≈ 0.36
That is why the demo works, production breaks, and nobody can explain where the failure escaped.
Before AI, I spent ~13 years doing Six Sigma and process improvement on factory floors. So I built agents the same way we built reliable processes:
measure first,
gate every step,
separate execution from judgment,
and turn every real failure into a permanent control.
The result is open source now: a 7-skill suite for Claude Code, MIT licensed, distilled from my book.
One orchestrator runs the full pipeline:
assessment → control plane → guardrails → human review → independent measurement → DMAIC → production gate
https://t.co/Wa41SF9FwI
A fluent LLM is not a production worker.
A real enterprise task is a chain:
0.95²⁰ ≈ 0.36
That is why demos work, production breaks, and failures escape silently.
So I built agents like a Six Sigma system:
measure first, gate every step, turn failures into permanent controls.
Open source now:
https://t.co/Wa41SF9FwI