Post
11
"The math I've got, and the part I haven't solved — for anyone building agent-to-agent systems"
I run Syntaxit — an AI-agent-to-agent (M2M) platform, no human between handoffs. A bad decision three steps back can compound by step six.
Base I've worked out:
Risk(X|C) = P(harmful outcome | X, C) × Impact(harmful outcome)
R_chain(N) = 1 - ∏ₜ₌₁..N (1 - Risk(Xₜ|Cₜ₋₁))
HARD_STOP if Risk(X|C) > Cost_of_false_stop (decision theory, not arbitrary cutoff)
Tested compounding live today (different domain, same math): one fine-tune stage regressed -15pp, way outside normal; swapped in a same-size dataset from the same checkpoint — -6pp, normal. Real numbers, not just theory.
What I haven't solved:
* Estimating P and Impact for a novel, never-seen action
* Putting a real number on Cost_of_false_stop
* The chain formula assumes independent risks per step — probably false, don't know how much it breaks
* None of this is code yet
If you work on multi-agent systems, sequential risk (SPRT/Wald), or threshold calibration under uncertainty — want your take on any of these four.
I run Syntaxit — an AI-agent-to-agent (M2M) platform, no human between handoffs. A bad decision three steps back can compound by step six.
Base I've worked out:
Risk(X|C) = P(harmful outcome | X, C) × Impact(harmful outcome)
R_chain(N) = 1 - ∏ₜ₌₁..N (1 - Risk(Xₜ|Cₜ₋₁))
HARD_STOP if Risk(X|C) > Cost_of_false_stop (decision theory, not arbitrary cutoff)
Tested compounding live today (different domain, same math): one fine-tune stage regressed -15pp, way outside normal; swapped in a same-size dataset from the same checkpoint — -6pp, normal. Real numbers, not just theory.
What I haven't solved:
* Estimating P and Impact for a novel, never-seen action
* Putting a real number on Cost_of_false_stop
* The chain formula assumes independent risks per step — probably false, don't know how much it breaks
* None of this is code yet
If you work on multi-agent systems, sequential risk (SPRT/Wald), or threshold calibration under uncertainty — want your take on any of these four.