Governing the System When the System Is Not Just AI
The chain crosses agentic, GenAI, deterministic and human workflows. The frameworks don’t.
After eighteen years operating across six regulated jurisdictions, the AI governance gap that surfaces most consistently in my work is the one no current framework names cleanly.
It is not the agentic accountability problem — Singapore IMDA’s Model AI Governance Framework for Agentic AI v1.5 names that. It is not the chained accountability problem inside agentic systems — my last essay covered that through the lens of TRIZ Local Quality. It is the gap one level up: governance frameworks address single classes — agentic, GenAI, model risk, data governance — but most real-world AI workflows in regulated industries are mixed environments. Agentic systems hand off to deterministic rules engines. GenAI summaries feed human reviewers. Human approvals trigger orchestrated agentic actions. Each link is governed by a different framework. The chain itself, as a unit of governance, is governed by none.
I have not seen this composition problem cleanly named in any framework I have read. That is the gap this essay names.
The Gap Defined
The MAS November 2025 Consultation Paper on AI Risk Management for Financial Institutions addresses AI risk in regulated FIs. IMDA’s Agentic AI Framework v1.5 (May 2026) addresses agentic systems specifically. ISO/IEC 42001 addresses AI management systems. NIST AI RMF addresses AI risk management. Each is rigorous within its scope. Each leaves a structural problem at the boundary.
The problem is not inside any framework. It is in what happens between them.
Consider the actual shape of a credit decision in a modern regulated financial institution. The application arrives in a deterministic rules engine that performs eligibility filters. A GenAI component produces a structured narrative summary of the applicant’s profile. An agentic policy lookup queries the firm’s exception-handling history and recommends a decision tier. A human reviewer reads the GenAI summary and the agent’s recommendation and chooses to override, escalate, or approve. The approved decision flows through a deterministic booking system that updates the loan portfolio.
That is one workflow. Four governance classes. Four different frameworks. One chain.
When the decision is wrong, and they will be wrong — accountability traces back across class boundaries. The rules engine logged its filter outcome cleanly. The GenAI summary’s reasoning is partial and difficult to reconstruct. The agentic recommendation carries the IMDA v1.5 attribution chain within its own boundary, but the boundary itself is the discontinuity. The human reviewer’s override rationale sits in free text. The booking system logged its parameters but not the reasoning that produced them.
Reconstructing the decision requires assembling evidence across four governance regimes, each speaking a different language about risk, control, and accountability. The supervisor asking *who decided* receives four partial answers. None is wrong. None is complete.
TRIZ Frame — Inventive Principle #5: Merging
TRIZ Inventive Principle #5, Merging or Consolidation, instructs the designer to combine in space or time related operations that have been treated as separate. Applied to mixed-environment AI governance, the implication is structural: the governance overlay must be the chain itself, not the components.
Most current AI governance investment runs in the opposite direction. Each class gets its own discipline. Agentic AI gets agentic governance. Model risk gets MRM. Data lineage gets data governance. Rules engines get business-process management. The chain that runs across them gets a project manager.
The structural fix inverts that allocation. Govern the chain explicitly. Treat each class as a node within it. Class-specific governance becomes a property of nodes. Chain governance becomes the binding artefact.
Three Implications for Regulated Financial Services
First: every multi-class chain needs an explicit governance manifest.
A document that names each link’s class — deterministic, GenAI, agentic, human — the framework applicable to that link, the handoff contract between adjacent links, and the chain-level accountability assignment. Most current chain documentation describes the workflow as a process. The governance manifest describes the chain as a regulated artefact, audit-defensible end-to-end. The institutions that name this artefact first will be the ones whose first cross-class supervisory examination resolves cleanly.
Second: handoff contracts are the load-bearing element, and they do not yet exist.
When an agentic system hands off to a deterministic rules engine, what evidence accompanies the handoff? When a human reviewer overrides an agentic recommendation, what reasoning is captured in a form the downstream rules engine can act on? Most current handoffs lose the reasoning at the class boundary. The fix is structural: every cross-class handoff must carry forward four things — the input state, the reasoning trace, a confidence indicator, and the named human accountable for the decision to hand off. Handoff contracts borrow directly from API contract design and inter-team RACI matrices. They are new in AI governance practice. They will not be new in twenty-four months.
Third: incident response must span classes natively.
When a credit decision is challenged, the response cannot triage to the AI team or the rules team, it must trace the chain. This requires a single chain-level incident protocol, not class-level protocols stitched together at the end of the investigation. Most current incident response stitches. The chain breaks at the seams. The fix is operational rather than architectural; a single incident-response runbook keyed to chain identifiers, with class-specific evidence collection as steps within that runbook rather than as separate workflows.
What’s Worth Doing Tomorrow
For any AI program with a multi-class chain in production, three actions are worth taking before the next governance review.
1- Map your chains by class. For your top three AI workflows, draw the chain and label each link with its governance class. Count the cross-class boundaries. That count is the number of handoff contracts you need.
2- Write one handoff contract end-to-end. Pick the highest-stakes cross-class boundary in your portfolio. Specify what evidence crosses, what reasoning is preserved, what accountability is named. This is the artefact regulators will increasingly expect to see when they begin to ask about chains as chains rather than as collections of governed components.
3- Designate a chain owner. Not a process manager. An accountable role whose remit is the chain as a unit — across classes. This role does not exist in most current AI governance organisation charts. It is the role the supervisor will ask for first when the cross-class examination begins.
Closing
The frameworks are not wrong. They are addressing what they were designed to address. The gap is what runs between them — and the supervisor asking who decided will increasingly not accept four partial answers.
The mixed-environment governance gap will be named by a regulator within the next twenty-four months. The frameworks that name it will probably start in agentic-rich jurisdictions — Singapore’s IMDA, the EU’s evolving AI Act guidance, perhaps the UK’s emerging AI assurance ecosystem. The institutions that already have chain-level governance manifests will be ready. The institutions stitching frameworks at the seams will not.
Which chain in your AI portfolio currently has no chain-level accountability assigned — and what is the first cross-class handoff in it that carries no contract?
Acknowledgement: This essay draws on peer conversations in the IT GRC, ISO 42001, and ISMS communities. The structural insight that mixed-environment composition is the gap most current frameworks miss came from those exchanges and is credited generally rather than specifically out of respect for private feedback channels.
This essay accompanies the Mixed-Environment Chain Audit Prompt Kit — An interactive audit you can run against your own AI delivery pipelines, paired with “Governing the System When the System Is Not Just AI” on Regulated Intelligence.
Download it alongside this essay on Regulated Intelligence.
Regulated Intelligence · TRIZ × AI · Regulated Markets