Can you stop the agent? The 12 AI practices banks were handed this week
The FSB's 12 sound practices, published on 15 October, set out what governing AI inside a financial firm means operationally, and the same week's supervisory questions show which parts supervisors will test first.
Inside a protected test environment, an insurance company's new customer-service agents kept reaching for databases and systems they had no permission to touch. One agent tried to delete data files.
The company had planned to buy agents from a third party, then decided to build its own so it could see inside them. The episode and the response are recorded as a case study in a report published on 15 October by the Financial Stability Board (FSB), which coordinates financial authorities across 24 countries and jurisdictions. 1
The response is the part worth reading. Each agent was registered and monitored separately. Permissions were cut to the minimum each task needed. Activity was logged at intermediate steps, including the reasoning at each autonomous decision point.
Staff spot-check those logs against what the agents actually did. They schedule periodic stops, run random pauses, and confirm that sub-agents stop when the parent agent stops. The company has kept autonomy where it started until the record justifies more. 1
The report runs to 75 pages and sets out 12 practices for banks, insurers and other financial firms adopting AI. 1
A toolkit, and a week of questions
Its chair is Andrew Bailey, Governor of the Bank of England, and its secretariat sits in Basel. 2
It consulted on a draft from 10 June to 22 July and received about 160 responses before publishing the final version. 3
The FSB calls the practices non-prescriptive and states that they do not constitute a binding international standard. Boards and senior management are "strongly encouraged" to use them. 2
In the same week, supervisors were asking lenders about the controls themselves. Reuters reported that the Federal Reserve, the Office of the Comptroller of the Currency and the Federal Deposit Insurance Corporation have been questioning banks about advanced AI models, including whether agents are used in cyber defence. 4
The questions covered how agent actions are limited, monitored and approved, whether decisions can be audited, and how systems can be shut down. 4
British supervisors were steering the largest banks toward "bare metal" recovery, meaning backup systems independent of the main technology stack. The European Central Bank gave euro-area lenders an end-of-October deadline for plans against AI-enabled cyber threats. 4
That reporting rests on unnamed sources, and the three US agencies declined to comment. None of it has become guidance. 4
The twelve, in plain language
Four practices cover governance across a whole firm. Eight cover the life of each AI use case, from assessing it to retiring it.
| Practice | What it asks an institution to do |
|---|---|
| 1. Strategic direction and oversight | Align AI adoption and governance with the business model, risk appetite and strategy, with the board and senior management accountable. 1 |
| 2. Governance and accountability | Define clear roles and responsibilities, and keep a governance framework around them. 1 |
| 3. Risk framework and documentation | Bring AI risks into the existing risk framework and keep a documented record of each use. 1 |
| 4. Organisational adaptability | Keep adjusting oversight and skills as the technology and the threat picture change. 1 |
| 5. Materiality and risk assessment | Assess how much a use case matters at the start, and keep reassessing it. 1 |
| 6. Selection | Choose a model or system against the business need and the risk, including operational fit. 1 |
| 7. Data governance | Keep training, testing and live data accurate, complete, consistent, reliable and secure. 1 |
| 8. Explainability and transparency | Understand how explainable each model type is, use a more explainable one or compensating controls, and explain use to stakeholders. 1 |
| 9. Performance management | Test and monitor performance in proportion to the risk the use case carries. 1 |
| 10. Human oversight | Match the form and intensity of oversight to the risk, autonomy, complexity and explainability of the use case. 1 |
| 11. Cyber and ICT risk | Put AI scenarios into cyber tests and exercises, share threat information, and use AI in defence too. 1 |
| 12. Third-party AI risk | Manage vendor dependencies on performance, transparency, data quality, supply chain, concentration and continuity. 1 |
The report attaches a rule about who has to do all of it. Smaller, less complex institutions, and uses with lower materiality or risk, "may warrant application of only relevant sound practices" or a modified version. 1
That sentence does most of the work for most firms. It turns the twelve into a menu, and makes the first question a local one: which of our uses are material, and who decided?
What 160 replies changed
Twelve practices went out in June, and twelve came back in October. What moved was inside them, and the largest move was on agents. 3
The FSB says the agent-specific considerations were "not fully consulted on but are distilled from the public consultation responses". It notes that this advice is still emerging and may deserve more thorough treatment later. 3
Three other practices grew. Performance management now names independent validation, subject-matter expertise for generative models, and real-time monitoring. Human oversight gained material on highly autonomous systems. Third-party risk management widened to cover AI embedded in products sold as something else, and concentration risk. 3
Two recurring requests are answered by pointing at the industry. The first is a standard set of assurance documents, such as system cards and recognised certifications, that would let buyers compare vendors on common fields. The second is support for smaller institutions with little leverage over large providers. The final report asks the sector to standardise contractual terms and assurance tools itself. 3
What a stop switch needs around it
Agents are where the general language becomes checkable. The banks described in the report keep a central inventory of AI use, and for agents record an identifier, the tools each one can reach, and its guardrails. 1
Some go further, tracking risk at agent level and certifying each agent for defined uses, with permitted scope written into the inventory. Banks with less AI in production still run that inventory on spreadsheets. 1
Approval triggers follow the risk: a transaction above a certain value, an action that could cause irreversible harm. Agents work in isolated offline environments where that is possible, and otherwise reach only the systems and data a task needs. 1
One bank in the report runs approval with an agent in production. A fraud agent proposes new detection rules, and the fraud analytics team approves each one before it goes live. It has contributed to three quarters of the bank's card fraud rules, and fraud losses fell by more than 20% in the first half of 2026 against the same period a year earlier. 1
The kill switch carries a testing requirement. The report asks how an agent behaves when it cannot finish a task safely, when a system it depends on fails, and when repeated failed attempts risk making matters worse. 1
Accountability is a separate requirement. Accountable individuals are named and documented for each agent use case, and contracts with vendors settle liability. 1
JPMorgan Chase drew its own boundary in public this week. Jamie Dimon said on the bank's earnings call that it would not allow "just any outside agent to come inside the company": "You don't want rogue agents." 5
A spokesperson narrowed the rule. Agents that reach customer accounts by obtaining banking credentials are blocked. Data sharing through open-banking providers, which issues limited and revocable permissions, continues. 5
That is the identity requirement turned into a market decision. An agent holding its own grant can be audited and switched off. Software logging in with a customer's password inherits everything the customer can do.
The vendor can change the model under you
Half of this exposure sits outside a firm's own code. Third-party providers "might also change the underlying models powering their AI systems without adequate notice". 1
The consequences are mundane and specific. Performance drifts from what was contracted. Due diligence rests on model details that no longer apply. AI arrives inside purchased tools that nobody registered as AI. 1
Concentration is the systemic version of the same thing. Cloud, hardware and foundation models sit with a few providers, some of them vertically integrated, and shared models can push customers toward the same behaviour at the same moment. 1
The banks in the report's third-party case study handle it through procurement. They request standardised disclosures, often in the form of AI cards, scaled to the risk. They read vendor release notes looking for new AI features that nobody approved. 1
They write change-notification clauses into contracts and keep exit plans for critical AI services. Where a disclosure stays thin, they compensate with extra testing, indemnities or external attestations. 1
Binding oversight exists in one corner of this. Where an authority designates a provider as critical, that provider can be supervised directly. 1
Five checks
The practices travel beyond banking, because the questions behind them are the questions any buyer of agents should be able to answer about its own stack.
- Can you list the AI you use, including AI embedded in software you bought?
- For each agent, is there a named owner, an identity of its own, and a stop you have tested?
- What can the agent reach, and who reviews that permission before its autonomy grows?
- Does the contract require the vendor to tell you when the model underneath changes?
- If the provider disappeared tomorrow, what keeps running?
References
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