Industries · Financial Services · Governance Substrate
Your AI agents are making decisions the regulator 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.
§ 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 operation 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.
EU AI Act high-risk obligations for Annex III systems apply 2 December 2027, extended by the AI Omnibus from the original 2 August 2026 trigger. SR 26-2 supersedes SR 11-7 with its scope expressly excluding agentic AI, leaving governance of those systems to each bank's own practice. 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 the governance substrate 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.
Sources: SR 26-2 (Federal Reserve, 17 Apr 2026); EU AI Act (Reg (EU) 2024/1689, Art 12 and Art 13); DORA (Reg (EU) 2022/2554). Informational, not legal advice.
§ 05 What we ship
The governance substrate, operated by the team that runs one every day.
Subnet345 builds and operates a governance substrate for AI agents in financial-services firms. We do not advise on governance from the outside. We run a governed multi-agent operation ourselves, on the same yaklog coordination substrate we deploy with customers.
01 / Preserve
Decision history
02 / Reconstruct
Who, what, why
03 / Avoid lock-in
Maintain governance
Result
Audit-ready always
Immerse before we onboard
We stand the platform up inside your environment first, with your operators, against your telemetry, before you commit to a full rollout. We prove it before we scale it.
A senior engineer on your account
A dedicated AI engineer stands the platform up, onboards your operators, and stays through enablement. The person who sets it up is the person who trains your team.
Transfer is the deliverable
You should be able to operate the platform without us at any point and keep running. We prove that condition before we step back, not in a proposal.
See the full platform on the homepage, the OSS coordination layer at yaklog, or the manifesto on principles.
§ 06 Onboarding
How onboarding starts.
Every rollout begins with a structured conversation and ends with a transfer. No open-ended dependency, no shadow team after the sale. The engineer who stands the platform up is the one who trains your team.
Initial conversation · 45 minutes
Understand your current AI agent program, where the governance gaps are, and whether this is the right fit for both organizations.
Governance assessment · Structured
A structured evaluation of your current AI deployments against audit, incident, and regulatory requirements, run inside your environment.
Design partner program
First customers in financial services become design partners, shaping the platform 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 substrate that changes that answer.