Regulated-Operations AI Governance · The Diagnostic
Can your AI agents survive a diligence review?
Most firms running agents in production believe they sit a rung higher than their audit chain can prove. Find out where yours actually sits — in two minutes.
It is the gap an LP’s diligence team, an acquirer’s tech DD, or an examiner probes first: who can override the model, and where is the proof. Usually invisible — until someone with the power to walk asks.
Principal
Your agents touch capital, mandates, or client decisions — and you carry the downside.
CTO / CCO
You’ll be the one asked who can override the model, and to show the audit trail.
PE Operating Partner
You’re carrying this same risk across a portfolio of companies, mostly unmeasured.
Run the A0→A4 self-scorer below. Seven questions, two minutes, a tier verdict on where your program actually sits: not a grade, a read on your exposure.
- $750M deal architect
- Ransomware-to-cloud: 50-day MVP, not 6 months
- 6 open libraries, all DOI-archived
Place your program on the ladder
Five maturity tiers, A0 to A4
Think of it as a driver’s license for AI. At A0 it only gives directions — a human drives. At A2 it drives inside a fenced lot while you spot-check. At A4 it drives itself on approved routes, with a brake it can’t switch off and a recorder of every turn.
A2 → A3 is the regulator-visible boundary. Escalation is automatic; de-escalation is deliberate. Most firms running agents in production believe they sit a rung higher than the audit chain can prove.
The compliance deadlines are real
Colorado SB 26-189
Effective August 12, 2026; compliance horizon January 1, 2027. The first U.S. cross-sector AI duty-of-care law a regulated operator has to answer to, covering risk management, disclosure, and a documented override path.
EU AI Act, Article 14
Human oversight is a named obligation for high-risk systems. If you serve EU clients or data, "the model decided" is not a defensible audit answer.
Model-risk supervision
Supervisory expectations for model risk are still catching up to agentic AI — and the counterparties who sell into regulated institutions inherit that diligence. Being able to show an effective-challenge story ahead of the rules hardening is the durable position.
These are deadlines, not talking points. The work to answer them takes longer than the gap between now and the dates above.
The patterns the diagnostic scores you against
Six permissively licensed pattern libraries — all six DOI-archived on Zenodo. These are the same governance primitives the diagnostic scores you against: public, free, and yours to read before any conversation. This is demonstration, not a gate.
Governance patterns for AI agents in regulated finance — DEFCON-style readiness state machine, sovereign veto, hash-chain audit, the autonomy ladder, EU AI Act mapping, shadow mode.
Standalone investment-adviser library — five primitives plus seven adviser controls mapped to the Advisers Act fiduciary duty. For buy-side and allocator AI.
Commercial real estate sibling — the same patterns plus three CRE-native gates: lease-abstraction provenance, fair-housing pre-flight, tenant-PII residency.
Banking primitives with model-risk effective-challenge, an ECOA / Reg B adverse-action gate, and an OFAC reference workflow wired by the deployer.
Healthcare-payer library — utilization and claims-adjacent governance for agents operating where coverage decisions and member PII meet regulation.
Payments library — real OFAC screening, BSA/AML and Reg E gates, plus a rail-finality / irreversibility check for actions that cannot be undone.
Your tier benchmark and the board-ready brief
Get your tier benchmark and the board-ready brief
The repos above are public. This is the part they aren’t: where your scored tier sits against the peer band for your firm type, plus a one-page brief written for the board. It covers the override question, the audit-chain question, and the deadline that applies to you. I send it to whatever inbox you use.
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