Catalog/Classes/Feynman
CLASS · 01 / 06v0.4Live · v0.4

Feynman

Show your work — every step, every assumption, every dependency.

Named afterRichard Feynman (1918 — 1988) — physicist who insisted that you don't understand a thing until you can explain it simply.

14d → 3d audit prep99.2% trace coverage4,100 decisions traced
01 — The science

Why we named it Feynman.

Feynman's core method was radical simplification — if you can't explain it simply, you don't understand it. The Feynman agent applies this to AI operations: every decision must be traceable to a reason a human can audit.

Not a black box with good outcomes. A glass box with accountable steps. "What I cannot create, I do not understand" means: if the agent can't reconstruct its own reasoning on demand, it doesn't run.

Feynman agents ship with full trace by default. Every conclusion has a path you can defend in a regulatory review — not because we instrumented it after the fact, but because the trace is the contract.

  • 01Feynman diagrams → explicit-interaction trace: every evidence-to-conclusion step is a discrete, inspectable node. No hidden terms.
  • 02Path integrals → multi-hypothesis reasoning: enumerate and weight all candidate explanations before collapsing to an answer; retain the unselected paths.
  • 03Challenger / Appendix F → evidence-before-narrative: conclusions are built from an evidence graph; the narrative is generated last, never first.

Read the design research →

Feynman class

What I cannot create, I do not understand.

— RICHARD FEYNMAN · LAST BLACKBOARD, 1988
Feynman class
1 / 4Why is it called Feynman?

What I cannot create, I do not understand. Show your work — every step, every assumption, every dependency.

Richard Feynman · Last Blackboard, 1988
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Field Evidence
ACADEMIC BASIS
"Language Models Don't Always Say What They Think"CoT explanations systematically misrepresent prediction reasons — biasing features shifted accuracy up to 36% while explanations omitted the bias.
Turpin, Michael, Perez & Bowman · NeurIPS 2023
IN PRODUCTION
Zest AI — explainable ML credit underwritingPer-decision reason codes (ECOA/FCRA), feature-level explanations, and examination-ready documentation in regulated production.
First Hawaiian Bank case study · zest.ai, 2025
BENCHMARK
Automated decisioning 4% → 55% (13×)Instant approvals up 9×, total approvals +25%; Zest-scored accounts outperform exceptions ~4× on delinquency. Fleet avg ~20% approval lift at constant risk (vendor claim).
Zest AI / First Hawaiian Bank, 2025
02 — Agents in this class

Prototype agents.

Every class ships with reference agents calibrated to operational use cases. Fork them, deploy them, or use them as a template.

Regulatory Decision Tracer

0 unexplainable decisions

Credit Decision Explainer

memo drafting −82%

Contract Clause Reasoner

7.1× review throughput

Root-Cause Narrator

time-to-explained −67%
12-WEEK BETA · 9 DESIGN PARTNERS · 47,000 SHADOWED RUNS
03 — Qualification gate

The ALOFT pipeline, applied.

Every agent in this class passes the same five-stage gate. Below: the criteria specific to Feynman agents at each stage.

ALOFT
01 · Curation
02 · Staging
03 · Deploy
04 · Operate
05 · Generalise
A→L→O→F→T
01
Curation
Audit trail required; expert sign-off
  • Use case requires audit trail
  • Risk tier ≥ tier-2
  • Domain expert sign-off
02
Staging
Trace >95%; handoff tested
  • Full trace coverage > 95%
  • Human handoff tested
  • Audit log schema validated
03
Deployment
Registry signed; scope ≤ tier-2
  • Registry entry signed
  • Rollback contract attached
  • Scope grant ≤ tier-2
04
Operation
Trace gaps <0.5%; latency <800ms
  • Trace gaps < 0.5%
  • Handoff latency < 800ms
  • Schema drift = 0
05
Generalisation
Pattern reused ×3; memo published
  • Trace pattern reused × 3
  • New domain qualified
  • Coaching memo published

Ready to deploy a Feynman agent?

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