We are over-relying on stochastic reasoning, and paying for it in three currencies at once: precision, quality, and cost. Every time an agent re-derives a decision it has already made correctly a hundred times, it pays full inference price for a worse answer than a lookup would have given.
Worse, a step left to inference is a step you cannot check before it runs, cannot replay afterwards, and cannot explain to anyone who asks why.
It is noticing how much of the work was never stochastic to begin with.
The sequence an agent follows every Tuesday is a machine.
A policy about what it may touch is a predicate.
A rule mined out of your own history is an artifact you can score against a corpus before you adopt it, and re-score later when the corpus has moved.
In that setting, learning is replay, not retraining. The rule set complements the weights.
One agent, or a crew of them, doing the work of a business — with the deterministic parts pulled out of the model and into artifacts you can check, replay and explain.
Those four are not new ideas dropped on top of an agent. Each one is the deterministic form of something the agent is already doing badly, at full price.
A rule that has held across your last two hundred runs is a lookup, not an inference. Mine it out of your own history, score it against a corpus before you adopt it, and re-score it when the corpus has moved.
What an agent may touch is a predicate over a policy — checkable before the agent starts rather than regrettable after. Either every path to your secrets crosses a gate, or here is the concrete path that doesn't.
An agent that rebuilds its own to-do list every run will rebuild it differently every run. A workflow is that plan as a machine, admitted only if every path through it passes — and a crew shares one run without doing anything twice.
Explore what a decision does against a virtual clock, across a fleet, before it reaches anything real. Every result replays exactly from engine version, scenario, seed and replication index.
Expertise, governance, workflows and simulation all come down to state machines — and the machines are the part an agent now writes faster than anyone can read. Polygraph explores every reachable state over declared domains and returns the shortest sequence that breaks a stated rule: a counterexample you can replay, not a hunch. No model sits on the decision path.
Cognitive Fab is built on the belief that the deterministic share of an agent's work is far larger than we currently assume — and that pulling it out, into machines checked before they load and rules that carry their own evidence, is what turns an agent from something you hope about into something you can hold to its mandate.
Jean-Jacques Dubray, Ph.D. · author of the SAM pattern · the approach · Provable Trust, the blog
Advisory, training and hands-on builds from the author of the SAM pattern — not a menu, a map: pick a corner, or blend a path across it.
Expertise, governance, workflows and simulation — installed hands-on, on your own code and systems, not toy examples.
half-day to two-day · remote or on-site 04 · BUILDWe build it in your stack, with your team — rules mined, policies proven, workflows admitted, twins running.
scoped build · your stack 01 · ASSESSWhere your systems and agents lean on inference today — and what to pull into rules, workflows and policy first.
fixed scope · written assessment + roadmap 03 · EMBEDThe four pillars embedded in your lifecycle, with your hardest machines and policies reviewed on call.
monthly retainer · continuousA 30-minute conversation about where AI-written code is quietly accumulating risk in your systems — and whether verification is the answer.