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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The right AI agent platform is the one your bank can constrain, observe, audit, and stop—not simply the one that produces the most impressive demo. Evaluate it against a defined use case and authority level, inventory its data and tools, set accountable owners and risk appetite, test it in your environment, and require evidence for security, resilience, oversight, monitoring, and exit. Then govern the agent throughout its lifecycle, including after provider or model changes.
Start with the use case and the agent’s authority
Do not begin with a feature checklist or a vendor ranking. Begin with a written use-case record. An agent that drafts an internal report presents a different risk from one that can change a customer’s account, approve a payment, or call a lending workflow.
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Document the intended job
Record the business outcome, users, jurisdictions, affected customers, expected volume, operating hours, and the failure that would matter most. State whether the agent is advisory, performs work under approval, or may act autonomously. “Customer-service assistant” is too broad; “retrieve card-dispute status from the case system and draft a response for a trained employee” is testable.
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Set an authority level
Describe exactly what the agent may decide, recommend, write, send, change, or purchase. Separate read access from write access and recommendations from commitments. Define transaction and data thresholds, prohibited actions, required approvals, and the conditions that force a hand-off. If the agent can initiate a payment or execute an instruction, treat that as a higher-impact design than an agent that only prepares a proposed action.
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Map the failure path
For each important task, specify what happens when the model is uncertain, hallucinates, receives conflicting instructions, loses a tool, or returns an unsafe result. The record should identify the customer or process affected, the person who can intervene, how quickly intervention must occur, and whether an action can be reversed.
Build an evidence-based comparison matrix
Use one matrix for procurement, risk, security, compliance, operations, and the business owner. Each requirement should point to a provider artifact or your own test result, have an internal owner, carry a risk rating, and show any unresolved exception and approval decision. A sales presentation is not evidence of suitability.
| Comparison area | Questions to answer | Evidence to request or produce |
|---|---|---|
| Use-case fit and authority | Can the platform perform the proposed task reliably? What tools can it call? How does it handle conflicting instructions or tool results? | Task evaluation, red-team results, permission map, failure and escalation tests |
| Data handling and security | What data enters the service, where is it processed, how are system boundaries enforced, and what are the retention, deletion, confidentiality, and access terms? | Contract terms, data-flow diagram, security reports, access-control design, incident process |
| Testing and oversight | Can the bank test before production, inspect decisions and actions, require human approval, and suspend the agent? | Evaluation plan, approval workflow, action logs, override and suspension demonstration |
| Monitoring and change management | Who monitors quality and misuse? How are material model, prompt, tool, or infrastructure changes disclosed and approved? | Service-level and change-notification terms, dashboards, alert thresholds, incident runbook |
| Provider and concentration risk | Can the provider operate and recover the service? Which subcontractors are involved, and how much of the bank would depend on one provider? | Financial and operational information, continuity and recovery tests, subcontractor register, concentration assessment |
| Integration, portability, and exit | Can the platform interoperate with bank systems? Can records, prompts, configurations, and evaluation data be exported? What happens during disruption or replacement? | Architecture, supported interfaces, export test, exit plan, transition assistance and deletion evidence |
| Governance fit | Who is accountable, which control functions review the service, and how are exceptions and risk appetite decisions reported? | RACI, inventory entry, approval record, board or senior-management reporting design |
Score each row for the specific use case rather than giving the platform a universal grade. A platform can be suitable for low-impact retrieval and unsuitable for autonomous payment activity.
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United States: model guidance is not an agent rulebook
The Federal Reserve’s current supervisory page describes revised interagency model risk guidance from the Federal Reserve Board, OCC, and FDIC as risk-based and tailored to an organization’s size, model risk, and operational complexity. It is most relevant to organizations above $30 billion in assets, while potentially relevant to smaller banks with significant model-risk exposure. It is supervisory guidance, not an enforceable standard. See the Federal Reserve guidance page.
The guidance defines a model in a way that excludes deterministic rule-based processes without statistical, economic, or financial theories underlying their design or use. More importantly for agent procurement, it states: “Generative AI and agentic AI models are novel and rapidly evolving. As such, they are not within the scope of this guidance.” That does not make agentic systems ungoverned. Banks should apply their existing governance, risk-management, security, third-party, and operational controls to systems outside the document’s scope.
The OCC’s 2026 announcement describes a planned request for information that will consider banks’ use of AI, including generative and agentic AI. A planned request is not issued agent-specific supervisory guidance and should not be treated as a new requirement already in force.
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United States: third-party oversight remains relevant
The interagency Guidance on Third-Party Relationships is directly useful when the platform is supplied or operated by another company. It calls for due diligence on the provider’s ability to perform as expected, follow bank policies, comply with law, and operate safely. It also addresses information systems, resilience, business continuity and recovery, incident reporting, subcontractors, interoperability, concentration, ongoing monitoring, independent review, and lifecycle documentation.
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Singapore: phased expectations for all financial institutions
Singapore’s Monetary Authority announced supervisory expectations for responsible AI adoption on 7 October 2026. The expectations apply to all financial institutions and forms of AI, using risk-proportionate controls. They call for clear accountability and risk appetite, appropriately granular AI inventories, use-case materiality assessment, and lifecycle controls for data governance, testing, human oversight, cybersecurity, monitoring, and change management. Financial institutions remain accountable for third-party AI used in services they deliver.
MAS says sections 3–4 take effect on 7 October 2027 and sections 5–6 by 7 October 2028. These dates are Singapore-specific; they do not automatically create obligations for a bank in another jurisdiction. The announcement is available at MAS’s responsible-AI release.
Deputy Managing Director Ho Hern Shin summarized the rationale: “AI has significant potential to improve financial services, from enhancing customer outcomes and strengthening risk management to improving productivity and enabling new products and services. Realising these benefits sustainably requires financial institutions to understand and manage the risks that come with increasingly capable AI systems.”
Broader context: adapt existing controls
The BIS report Governance of AI adoption in central banks highlights data security and confidentiality, hallucination-related model risk, and reputational risk. It describes adapting existing frameworks, including the three lines of defence, to AI use cases. It concerns central banks, so use it as governance context rather than as a commercial-bank supervisory requirement.
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The IMF’s April 2026 note, How Agentic AI Will Reshape Payments, is analytical policy material rather than binding regulation. It explains that probabilistic systems can produce different outputs from identical inputs and examines agents that initiate transactions or execute payment instructions. Its controls—human approval or supervisory intervention for high-risk or high-value transactions, thresholds, dashboards, manual overrides, and interruption mechanisms—are design considerations to assess against the bank’s risk appetite.
Examine data, security, and confidentiality boundaries
Draw the complete data-flow map
List every input and output: customer records, transaction data, credentials, prompts, retrieved documents, tool responses, generated text, logs, evaluation sets, and telemetry. Mark where each item is collected, processed, stored, backed up, and deleted. Include the platform, model host, plug-ins, orchestration layer, observability service, support channel, and every subcontractor.
Ask whether customer data is used to train or improve a shared model, whether the bank can opt out, how tenant isolation works, and which personnel can access content. Obtain retention and deletion commitments that cover production data, backups, caches, logs, and derived artifacts. Confirm how legal holds, records-management duties, and data-subject requests interact with deletion.
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Test identity and least privilege
Require separate identities for users, agents, tools, and administrators. Check whether permissions are scoped by task, customer, account, geography, and environment. A read-only support agent should not inherit a service account that can alter a core record. Test token lifetime, secret storage, privileged access, break-glass access, and revocation.
Request usable security evidence
Review independent assurance, penetration-test summaries, vulnerability management, secure-development practices, encryption, key management, logging, and incident response. Ask what constitutes a security incident, when the bank is notified, what information is supplied, and how joint investigation works. Evidence must be current enough for the proposed deployment and specific enough to show which service and region it covers.
Test the agent before granting production access
Define acceptance criteria
For each task, create a representative evaluation set containing normal cases, edge cases, ambiguous requests, prohibited requests, conflicting instructions, unavailable tools, stale data, and adversarial content. Measure the outcomes that matter to the use case: factual accuracy, correct tool selection, policy adherence, escalation, latency, completeness, and harmful-action rate. Do not publish a generic “accuracy” number unless its task, sample, date, and measurement method are stated.
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Run permission and instruction-conflict tests
Attempt to make the agent reveal hidden instructions, use an unauthorized tool, follow malicious text in a retrieved document, bypass an approval, or continue after a failed control. Check whether it distinguishes a customer’s request from an instruction embedded in data. Verify that the orchestration layer enforces permissions independently of the model’s answer.
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- Sandbox: connect synthetic or carefully minimized data and block external side effects.
- Shadow mode: let the agent produce recommendations while existing staff or systems make the real decision.
- Limited pilot: restrict users, products, regions, transaction values, tools, and operating hours; require enhanced review.
- Production: enable only the permissions that passed testing, with monitoring, escalation, and a tested suspension procedure.
Re-test after changing the model, system prompt, retrieval index, tool, policy, hosting region, or major dependency. A platform’s prior test result does not automatically cover a materially changed configuration.
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Place approval where it changes the risk
Human review should occur before an irreversible or high-impact action, not merely after an agent has sent it. Define which transactions, customer outcomes, disclosures, exceptions, and policy decisions require approval. Use thresholds by value, velocity, customer vulnerability, novelty, and confidence or evidence quality.
Make intervention practical
Operators need a dashboard showing the request, retrieved context, proposed action, tool calls, approvals, policy checks, and current state. They need to pause a run, revoke credentials, cancel queued work, and prevent retries. A “kill switch” is useful only if its location, authority, response time, and effect on in-flight work are tested.
Human approval can delay payments or other time-sensitive operations. Assess liquidity, settlement, customer-service, and staffing consequences before setting approval gates. Where delay itself creates harm, use lower-risk limits, pre-authorized actions, or a second automated control rather than assuming that a person can review everything immediately.
Preserve accountability
Assign an accountable executive and named owners for the business process, model or agent, data, security, compliance, operations, and provider relationship. The person approving deployment must be able to see unresolved exceptions and understand the residual risk. Keep an inventory entry at a granularity that distinguishes materially different agents, configurations, and uses.
Monitor performance, incidents, and change
Monitor the whole system
Track task outcomes, escalation rates, overrides, unauthorized attempts, tool failures, latency, availability, data-quality problems, drift, and customer complaints. Sample conversations and actions under appropriate privacy controls. Compare production behavior with the acceptance baseline and investigate unexplained changes.
Define incident and escalation paths
Write severity levels and notification clocks for incorrect advice, unauthorized access, policy bypass, harmful transactions, data exposure, prolonged outage, and provider compromise. Identify who can freeze the agent, who communicates with customers and regulators where required, how evidence is preserved, and how affected actions are corrected.
Control changes
Require notice and risk assessment for model replacement, version changes, prompt or policy edits, retrieval-data changes, new tools, subcontractor changes, hosting-region moves, and material security events. Decide which changes require re-approval, regression testing, or a new pilot. The bank’s change record should connect the deployed configuration to the tests and approvals that cover it.
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Assess provider, resilience, concentration, and exit risk
Look beyond the model
Evaluate the provider’s operational capability, financial condition, support coverage, recovery objectives, incident history, and ability to supply records. Identify every critical subcontractor and its location, role, access, and replacement difficulty. Determine whether one provider, model host, cloud, identity service, or data store creates a concentration that could affect several banking activities at once.
Test continuity and recovery
Ask for business-continuity and disaster-recovery plans, recent exercise results, dependencies, recovery time and recovery point objectives, and communication procedures. Run a bank-specific scenario: the agent is unavailable during a high-volume period, returns degraded results, or loses access to a critical tool. Confirm the manual fallback, staffing, customer communications, queued work, reconciliation, and safe restart steps.
Make exit executable
Specify the records and artifacts the bank can export: prompts, policies, configurations, tool definitions, logs, approvals, evaluation data, model outputs, and audit evidence. Test an export rather than accepting a contractual promise. Document replacement options, data transformation, parallel running, termination assistance, deletion certification, and how the bank will continue the service if a provider fails or changes terms.
Turn the evaluation into a decision
Use a risk-based gate
Set minimum evidence and approval requirements by impact tier. A low-impact internal drafting agent may need restricted data, review before use, and strong logging. An agent that can affect a customer account or payment should normally require stronger testing, independent challenge, transaction limits, approval or intervention, resilient operations, and a rehearsed shutdown.
Record residual risk, not just scores
For every unmet requirement, state the scenario, affected population, likelihood and impact assessment, compensating control, owner, deadline, and authority accepting the risk. “Provider does not yet support export of action traces” is actionable; “portability: medium” is not.
Report at the right level
Operational teams need live alerts and runbooks. Control functions need evidence, exceptions, incidents, and change history. Senior management and the board need the inventory, material uses, concentration, significant incidents, open high-risk issues, and whether the agent remains within risk appetite.
Quick Recap
A practical due-diligence sequence
- Classify the use: document purpose, users, jurisdictions, data, tools, decisions, transactions, and failure impact.
- Set authority: define read/write permissions, limits, approvals, prohibited actions, reversibility, and escalation.
- Map dependencies: identify provider, model host, cloud, data stores, tools, subcontractors, and internal owners.
- Collect evidence: obtain contracts, security and resilience material, incident terms, data-flow details, change commitments, and export capabilities.
- Test in context: run representative, adversarial, permission, failure, and recovery tests using the intended configuration.
- Approve with conditions: document residual risk, compensating controls, owners, expiry dates, and any pilot limits.
- Operate and review: monitor outcomes, incidents, overrides, drift, provider changes, and concentration; re-approve when risk changes.
- Exercise exit: verify fallback operations, exports, replacement steps, and deletion evidence before the service becomes indispensable.
Common evaluation mistakes
- Choosing the best demo: a fluent conversation does not prove safe tool use, reliable escalation, or recoverability.
- Scoping only the model: the material risk may sit in retrieval, credentials, orchestration, plug-ins, logs, or a subcontractor.
- Delegating accountability: provider reports and outside consultants inform due diligence but do not transfer the bank’s responsibility.
- Approving “human in the loop” without a workflow: name the approver, evidence shown, response time, authority, and override path.
- Ignoring change: a new model, prompt, index, tool, or hosting location can invalidate prior testing.
- Leaving exit until termination: if exports, staffing, and manual fallback have not been tested, the bank may be unable to leave safely.
- Using unverified market statistics: do not rely on an adoption percentage or performance claim unless the original population, wording, definitions, and date are available. The European Banking Authority’s 2025 factsheet search excerpt mentioning 55% adoption was not independently verified here and should not be used as a published statistic.
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