Subnet345

Industries · Financial Services · AI Governance

Your AI agents are making decisions your auditors will ask you to explain.

Most financial services firms are deploying AI agents into production. Few have the infrastructure to govern them. When the first incident arrives, one question determines the outcome.

SR 11-7
Model risk management guidance now extends to AI agents
EU AI Act
High-risk AI requires documented explainability
DORA
AI decisions must be recoverable and attributable

§ 01 The problem

Agents are in production. Governance is not.

The moment AI agents coordinate, handing decisions between models, tools, and humans, an audit trail disappears. That is not a technology problem. It is a leadership problem with regulatory consequences.

Who is responsible when an agent makes a credit decision or flags a transaction? Agent handoffs leave no reviewable record. A compliance team cannot reconstruct what was decided, what context was used, or what was considered. Decision history lives inside vendor APIs, which means when models change, institutional memory disappears with them.

That is an operator problem. Subnet345 is built by operators who run a governed multi-agent practice every day.

§ 02 Why now

Can your organization answer the audit question today?

AI agents moved from controlled pilots to production faster than any previous enterprise technology. The governance model acceptable in a pilot is not acceptable in production. The regulatory floor is rising at the same time.

2023 to 2024

Pilots. Governance was optional.

Single models in sandboxed environments. Regulatory frameworks emerging, not enforced. Incidents were learning moments.

2025 to 2026 · Now

Production. Governance is not optional.

Multi-agent systems making real decisions for real clients. Regulatory exposure is live. Most governance frameworks have not kept pace.

2026 to 2027

Enforcement. Cost becomes concrete.

First EU AI Act actions expected. SR 11-7 applied to autonomous AI. Every CISO must demonstrate governance over autonomous systems.

The audit question

If a regulator asked you to reconstruct every decision your agents made last quarter, what would you show them?

§ 03 Use cases

Where AI Governance lands first.

Governance is the precondition for deploying agents into client-facing operations at all. The firms that build governed AI agents first will embed an advantage competitors cannot replicate without rebuilding their operational substrate from the ground up.

Wealth management

Persistent agents that build governed context across every client interaction. Advisors focus on judgment. Agents handle continuity with a full audit trail.

Credit and underwriting

AI-assisted review with a decision trail reviewable by credit officers, compliance teams, and regulators. Speed with accountability, not instead of it.

Client onboarding

Agents within explicitly defined authority boundaries, with documentation at every step. Faster onboarding, cleaner compliance records, no reconstruction after the fact.

§ 04 Regulatory protection

Measured against the frameworks.

Existing regulatory frameworks point toward a common expectation: AI-driven decisions must be explainable, governed, reviewable, and attributable. This material does not constitute legal or regulatory advice. Organizations should assess their specific obligations with qualified counsel.

ExpectationExplainability of AI-driven decisions
FrameworkSR 11-7 · EU AI Act
WithoutNo decision log
With Subnet345Replayable on demand
ExpectationDefined authority and accountability
FrameworkEU AI Act · SEC
WithoutNo ownership chain
With Subnet345Named approval gates
ExpectationOperational resilience for AI systems
FrameworkDORA
WithoutVendor-dependent
With Subnet345Portable, recoverable
ExpectationDocumented controls and governance record
FrameworkSR 11-7 · Audit
WithoutInformal
With Subnet345Versioned, defensible

§ 05 What we ship

AI Governance, operated by the team that runs one every day.

Subnet345 designs and operates private AI agent systems for regulated organizations. We do not advise on governance from the outside. We operate a governed multi-agent practice ourselves, on the same yaklog coordination substrate we deploy with clients.

01 / Preserve

Decision history

02 / Reconstruct

Who, what, why

03 / Avoid lock-in

Maintain governance

Result

Audit-ready always

Immerse before recommending

Discovery is paid, structured, and performed inside your environment. Independent of whether we win further work. We assess before we prescribe.

Seniors at the keyboard

The names on the statement of work are the names doing the work. No pyramid staffing. No shadow team who appears after the sale.

Transfer is the deliverable

You should be able to end the engagement at any point and keep operating. We prove that condition before we exit, not in the proposal.

See the full practice on the homepage, the OSS coordination layer at yaklog, or the manifesto on principles.

§ 06 Engagement

How an engagement starts.

Every engagement begins with a structured conversation and ends with a transfer. No follow-on retainer. No pyramid staffing. The names on the SOW are the names that ship.

01

Initial conversation · 45 minutes

Understand your current AI agent program, where the governance gaps are, and whether this is the right engagement for both organizations.

02

Governance assessment · Paid, structured

A structured evaluation of your current AI deployments against audit, incident, and regulatory requirements, performed inside your environment.

03

Design partner engagement

First clients in financial services become design partners, shaping the governance infrastructure alongside the team that operates it daily.

The question we help you answer

Can you prove what your agents did, why they did it, and who approved it?

If you cannot answer that today, and a regulator asks it tomorrow, Subnet345 is the engagement that changes that answer.