Agentic AI Registry: The Foundation for Enterprise AI Governance and Risk

Gen AI and Agentic AI Registry: The Next Frontier of Model Risk

SR 26-2 does not govern generative AI or agentic AI use cases. This is explicit and deliberate. But the absence of a rulebook does not imply the absence of risk; and the regulators have signaled clearly that this deferral is temporary. For banks, insurers, and asset managers deploying large language models and autonomous agent systems at enterprise scale today, the governance gap is real and the window to define internal standards ahead of regulatory prescription is narrow.

The institutions that move now will not only be better prepared when formal guidance arrives, having already an operational foundation for robust AI Governance.

Every organization deploying AI at scale faces the same foundational challenge: knowing what it has, where it is running, and what it is connected to.

Enterprise AI deployments currently are scattered – a bank may run Microsoft Copilot across its operations teams, Amazon Bedrock for internal document summarization, and a custom GPT-X deployment for client-facing advisory support - each with different data connections, different risk profiles, and different teams who believe they own the governance question. Without a structured registration process, these systems accumulate invisibly. By the time a regulator or internal audit asks what AI the institution is running, the honest answer is often incomplete.

The Immediate Solution

The model risk management inventory - already the backbone of traditional MRM - is the natural home for GenAI and agentic AI registration. But the inventory needs to evolve as registering a large language model (LLM) or a use-case entails differences compared to a traditional credit scorecard model.

The record must capture not just the use case (the model under MRM) itself, but its prompt configuration, grounding sources, API dependencies, agent topology, monitoring and performance metrics and the specific use cases it is being applied to. An AI system inventory record must document that it is API-grounded, that it does not make eligibility determinations, that it is accessible only to authenticated users, and that a supervisory agent constrains its tool use. Without that level of detail, the inventory entry is just a label and not a true governance artefact.

Tiering as the Engine of Proportionate Governance

The materiality tiering logic introduced by SR 26-2 (U.S. Based entities) for traditional models applies equally to GenAI usecases and agentic systems. Not every AI use case carries the same risk – e.g. a sandboxed internal productivity tool is not the same as a client-facing system that responds to questions about medical coverage.

Governance that treats them identically wastes resources on the former and underserves the latter.

A structured four-tier risk model provides architecture for a risk-based governance approach. Tier 1 captures participant- or customer-facing systems, systems that influence regulated decisions, and systems with elevated legal, reputational, or ethical exposure - these require continuous monitoring, formal validation, and executive accountability. Tier 4 captures proof-of-concept and sandboxed use cases with no production deployment and no external exposure - these require basic inventory and usage guardrails, nothing more. The tiers in between calibrate governance depth to actual risk associated with the specific use case.

Critically, tier assignment should not be a one-time decision made at the time of intake and registration. It should also recalibrate whenever there is a material attributes change - a scope expansion, a new data connection, a change in the user population - and every reassignment should carry a documented rationale and audit trail.

Lifecycle Gates as the Control Mechanism

Speed is often cited as the reason AI governance is bypassed. A team moves a system from prototype to production before the governance record is complete because the business need is urgent and the approval process feels like friction.

SR 26-2's proportionality principle gives institutions a legitimate basis for faster approval pathways for lower-risk systems - but it does not remove the need for lifecycle gate enforcement for higher-risk ones. For a system in a conditional pre-production state pending final validation of testing, security, and operational readiness, the governance platform should actively block progression to the next lifecycle stage until each condition is resolved and evidenced. A conditional approval is not an approval, just a documented holding position with explicit outstanding requirements, and the platform should enforce that distinction automatically rather than relying on human discipline to remember it.

Monitoring Cannot Be an Afterthought

Traditional model monitoring tracks performance drift, stability, and outcomes over time. GenAI monitoring requires all of that and more. Hallucination detection, PHI and PII leakage monitoring, prompt injection risk, toxicity screening, and jailbreak detection are not edge cases - they are operational requirements for any system with participant or customer exposure.

For institutions already running observability infrastructure on platforms like AWS, the governance layer does not need to replace that capability but integrate with it: surfacing AI-specific risk signals into the governance record, triggering incident workflows when thresholds are breached, and maintaining a tamper-evident audit trail that connects a detected issue to the response taken and the outcome achieved.

What Regulators Will Look for When They Arrive

The EU AI Act is already in effect for high-risk AI categories, with stringent compliance consequences for institutions operating in relevant jurisdictions. PRA SS1/23 takes a more prescriptive approach to model governance than SR 26-2 and applies to UK-regulated entities regardless of where a model was developed. When US regulators turn their formal attention to GenAI and agentic AI - and the signals suggest that moment is approaching - they will not be starting from zero. They will be reviewing what institutions built in the interim and asking whether governance reflected actual risk or waited for instruction. The institutions best positioned will be those that treated the regulatory deferral not as permission to pause, but as an opportunity to define standards that will outlast whatever guidance eventually arrives.

How Solytics Partners Can Help: Turning Governance Intent into Operating Reality

The governance challenges created by SR 26-2's deferral of GenAI and agentic AI require not only policy interpretation, but also an operating infrastructure that can register, tier, validate, and monitor AI systems at the pace institutions are deploying them.

MRM Vault™ provides that foundation: a 360° inventory that captures not just the model itself but its agent topology, prompt configuration, grounding sources, API dependencies, and lifecycle stage. Materiality tiering is fully configurable to your scoring criteria, tier assignment recalculates automatically when material attributes change, and lifecycle gates actively block progression until every condition is resolved and evidenced.

For institutions operating across multiple jurisdictions - subject simultaneously to SR 26-2, PRA SS1/23, and the EU AI Act - auto-generated factsheets, Data Protection Impact Assessments, and structured evidence packs map governance activities to each applicable framework, producing a single audit record that satisfies multiple regulators without duplication of effort.

Nimbus Uno™ extends traditional model validation capabilities into the risk characteristics and drivers that define GenAI systems - hallucination detection, PHI and PII leakage monitoring, prompt injection risk, toxicity screening, and fairness assessment across relevant user cohorts.

For institutions already running observability infrastructure on AWS, Azure, or other platforms, MRM Vault™ integrates with existing engineering layers rather than replacing them, surfacing AI-specific risk signals into the governance record and triggering structured incident workflows when thresholds are breached. Native integrations with ServiceNow, Archer, GitHub, Jira, and CI/CD pipelines mean governance gate failures can programmatically block deployment pipelines without manual intervention - ensuring that governance built for speed does not become governance bypassed under pressure.

Related Topics

• MRM for Complex Organisations with Subsidiaries or Diverse Legal Entities
• Transitioning from Traditional ML Governance and Monitoring to GenAI Use Cases
• Treatment of Third-Party Models in MRM
• Are Low or Non-Risk Use Cases Built from GenAI Subject to MRM Practices?

References

1. Board of Governors of the Federal Reserve System, Office of the Comptroller of the Currency, and Federal Deposit Insurance Corporation (April 17, 2026). SR 26-2: Revised Guidance on Model Risk Management. - Link
2. Prudential Regulation Authority, Bank of England (May 17, 2023). SS1/23: Model Risk Management Principles for Banks. - Link