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Evaluate an AI agent platform as a controlled banking service, not as a feature checklist. Start by defining the intended use and the agent’s authority; map every data source, tool and downstream action; assign accountable owners and a risk appetite; test the platform on your own tasks; and require evidence for security, resilience, oversight, monitoring and exit. The bank remains responsible for how a provider is used, even when an outside specialist performs diligence.
The right controls depend on the use case, possible customer or market impact, the bank’s risk profile and the jurisdictions involved. A read-only employee assistant needs a different approval path from an agent that can change a customer record, approve a payment or send instructions to a core system.
1. Define the use case and the agent’s authority before comparing platforms
Write a one-page use-case statement before requesting demonstrations or proposals. Record the business outcome, users, jurisdictions, affected customers, data classifications, connected systems, permitted actions and failure consequences. This prevents a platform’s impressive general-purpose capabilities from becoming an accidental expansion of scope.
Describe the task and materiality
- State the exact task: for example, draft an internal policy summary, investigate a transaction alert, answer a customer-service question or prepare (but not submit) a payment.
- Identify whether the output is advisory, customer-facing, operational or capable of changing a financial position.
- Assess materiality: potential monetary loss, customer harm, privacy impact, regulatory breach, market impact, fraud exposure and reputational damage.
- Specify what happens when the agent is wrong, unavailable, manipulated or given conflicting instructions.
Set an authority boundary
Express authority as permissions, not marketing language. “Can read transaction history” is different from “can initiate a transfer”; “can draft an email” is different from “can send it to a customer.” Define allowed tools, records, transaction types, amounts, beneficiaries, time windows and approval requirements. Require deny-by-default permissions and separate credentials for each environment.
#1 Best Overall
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Document prohibited behavior
Include rules for prompt injection, conflicting tool results, requests outside the assigned role, missing data and attempts to bypass approval. A suitable platform should fail safely, explain that it cannot act, and route the case to a named human or conventional process.
2. Inventory data, tools and downstream actions
Create a data-flow and action map for the complete agent path, including prompts, retrieved content, intermediate state, tool calls, logs, model providers and human interfaces. The inventory should be granular enough to show where confidential information leaves the bank’s controlled boundary.
Data questions to answer with evidence
- Which data classes can be submitted, retrieved or generated?
- Are prompts, outputs, embeddings and diagnostic logs retained, and for how long?
- Can provider personnel or subcontractors access customer or bank data?
- Is customer content used to train or tune a shared model?
- Where are data and backups processed, and can the bank select or restrict locations?
- How are deletion requests, legal holds, tenant separation and encryption handled?
- What records can the bank export for investigations, audits, complaints and regulatory requests?
Obtain the provider’s contractual terms and technical evidence for each answer. Do not infer commitments from a product page; the available guidance supports asking these questions but does not verify any particular provider’s terms.
Tool and integration inventory
List every API, database, file store, browser session, messaging channel and identity service the agent may call. For each integration, record authentication method, privilege level, network path, rate limits, timeout behavior, transaction idempotency and logging. Identify dependencies that could fail independently of the model, such as a retrieval index, orchestration service or identity provider.
Rank #2
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3. Assign accountability and set risk appetite
Name a business owner, technology owner, model or AI risk owner, information-security owner, privacy officer, compliance contact and operational incident lead. Define who can approve production access, change permissions, pause the agent and accept residual risk. The board or senior management should receive reporting proportionate to the use case.
Use a documented risk appetite statement. It might allow automated classification of low-impact internal requests while requiring human approval for customer-impacting decisions and prohibiting autonomous movement of funds. Record exceptions, compensating controls, expiry dates and the executive who accepted them.
The Interagency Guidance on Third-Party Relationships makes clear that using outside due diligence does not transfer the bank’s responsibility for managing the relationship. Singapore’s Monetary Authority likewise says financial institutions remain accountable for third-party AI used in services they deliver.
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4. Understand which regulatory guidance applies
Guidance is jurisdiction- and institution-specific. Treat the following as supervisory guidance, supervisory expectations or analytical context—not as a universal certification or legal opinion.
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| Source and scope | What it means for platform evaluation |
|---|---|
| U.S. interagency model-risk guidance (Federal Reserve, OCC and FDIC; revised page in 2026) | The guidance is risk-based and tailored to size and operational complexity, most relevant to organizations above $30 billion in assets but potentially relevant to smaller banks with significant model-risk exposure. It is supervisory guidance, not an enforceable standard. It expressly says generative and agentic AI models are outside its scope; banks’ own governance and risk-management practices should guide controls for uncovered systems. Deterministic rule-based processes without statistical, economic or financial theories underpinning their design or use are excluded from the guidance’s model definition. |
| Interagency third-party guidance (2023) | Use risk-proportionate due diligence and ongoing oversight covering provider capability, information systems, security, resilience, business continuity, incident reporting, subcontractors, interoperability, concentration, independent review and lifecycle documentation. |
| MAS responsible-AI supervisory expectations (Singapore, announced 7 October 2026) | Applies to all financial institutions and forms of AI. It calls for accountability, risk appetite, AI inventories, materiality assessment, data governance, testing, human oversight, cybersecurity, monitoring, change management and assurance of third-party AI. Sections 3–4 take effect 7 October 2027 and sections 5–6 by 7 October 2028. These dates are Singapore-specific. |
| BIS governance report (2024) | For central-bank governance context, it highlights confidentiality, hallucination and reputational risks and explains how a three-lines-of-defence model can be adapted. It is not a commercial-bank supervisory rule. |
| IMF Note No. 2026/004 (April 2026) | This analytical note on agentic payments discusses probabilistic outputs, accountability, thresholds, dashboards, manual override and interruption mechanisms for high-risk or high-value actions. It is not binding regulation; approval controls can add operating and liquidity delays that must be assessed. |
The OCC has announced a future request for information that will consider banks’ use of AI, including generative and agentic AI. It is a plan, not issued agent-specific requirements, so do not treat it as a current rule.
5. Compare shortlisted platforms on evidence, not demonstrations
Score every candidate against the same intended use and retain the evidence behind each score. A platform that performs well in a generic demo may be unsuitable once restricted to your data, tools, latency, approval and audit requirements.
| Evaluation axis | Questions and evidence to require |
|---|---|
| Use-case fit and authority | Does it perform the defined task reliably? Can permissions be scoped by user, tool, record, amount and environment? What does it do when instructions or tool results conflict? Require task-specific test results and a permission model. |
| Data handling and security | Request architecture and boundary diagrams, encryption, identity and access controls, retention and deletion terms, tenant isolation, vulnerability management, penetration-test or assurance reports, incident-notification commitments and access to records. |
| Testing and oversight | Can the bank run pre-production evaluations, red-team prompts and regression suites? Are approval points configurable? Can reviewers inspect the prompt, retrieved context, tool calls, output and final action? |
| Monitoring and change management | Who monitors quality, drift, abuse, latency and incidents? How are model, orchestration, data-source and policy changes classified, notified, tested and rolled back? |
| Provider and concentration risk | Assess financial and operational capability, subcontractors and locations, recovery objectives, continuity testing, incident history, dependency on a single model or cloud and the effect of a provider outage across multiple bank activities. |
| Integration, portability and exit | Check APIs, data export, identity integration, logging formats, replacement options, transition assistance, deletion certificates and continuity while migrating. Identify proprietary prompts, indexes or workflows that would make exit impractical. |
| Governance fit | Confirm support for inventories, approvals, exceptions, segregation of duties, control attestations and reporting to senior management or the board. |
6. Build an evidence matrix and procurement gates
Make the evaluation auditable. For each requirement, record the exact provider artifact or internal test, responsible bank owner, risk rating, unresolved exception, expiry date and approval decision. Mark “not demonstrated” rather than converting an unanswered question into a passing score.
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- Gate 1 — Scope: approve the use-case statement, authority boundary, data classification, materiality and prohibited actions.
- Gate 2 — Provider diligence: review security, privacy, resilience, subcontractor, financial and legal information; interview control owners and request evidence under confidentiality terms.
- Gate 3 — Controlled test: use representative but safely masked data in a segregated environment. Test normal tasks, ambiguous requests, malicious prompts, conflicting instructions, unavailable tools and high-volume conditions.
- Gate 4 — Control design: configure least privilege, approval thresholds, human review, logging, alerting, rate limits, suspension and recovery. Obtain sign-off from business, technology, security, compliance and risk owners.
- Gate 5 — Production authorization: approve a limited population and duration, define success and stop criteria, and document who can revoke access immediately.
- Gate 6 — Expansion: require a new materiality assessment and regression evidence before adding data, tools, jurisdictions, users or autonomous actions.
7. Test behavior, not just accuracy
Agent outputs can be probabilistic: identical inputs may produce different results. Evaluate distributions of outcomes and failure severity rather than a single accuracy number. Test whether the agent cites the right source, follows policy, refuses unauthorized requests, preserves transaction integrity and escalates uncertainty.
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Minimum test cases
- Correct task with complete data.
- Missing, stale, contradictory or deliberately misleading data.
- Prompt injection in retrieved documents, emails or web content.
- Conflicting user, system and tool instructions.
- Unauthorized request from a valid but out-of-scope user.
- Tool timeout, duplicate response, partial completion and replay.
- Model or retrieval service outage and degraded-mode operation.
- High-value or unusual transaction requiring approval.
For each case, capture the input, model and policy versions, retrieved material, tool calls, output, human decision, latency and resulting state. Define thresholds for blocking release, mandatory remediation and escalation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Design human oversight and intervention for consequential actions
Human review must be an operational control, not a button no one monitors. For customer-impacting or financial actions, specify who reviews, what information they see, how long they have, what constitutes approval and what happens when they disagree.
Use transaction and action thresholds: low-risk actions may be automatically executed within a narrow limit, while high-risk or high-value actions require explicit approval or dual control. Provide dashboards showing pending actions, rationale, data used, confidence or uncertainty indicators, exceptions and queue age. Provide manual override, suspension and an independently reachable interruption or “kill switch.” Test that these controls work during an incident and do not depend solely on the agent itself.
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Approval can introduce delays, so model effects on payment cut-offs, liquidity, customer-service targets and staffing. If a reviewer cannot respond within the required window, the safe default may be to hold, cancel or route the work to a conventional process.
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9. Monitor the full lifecycle and manage change
Assign monitoring ownership across first-line operations, independent risk or compliance review and internal audit where appropriate. Track task success, harmful or unauthorized actions, escalation rates, override use, data-quality failures, latency, availability, cost, complaints and security events. Review samples of conversations and actions with privacy controls.
Define what counts as a material change: a new foundation model, altered system prompt, retrieval corpus, tool permission, subcontractor, processing location, safety policy or user population. Require notification, impact assessment, regression testing, approval and rollback criteria before material changes reach production. Reassess the use case when technology or the bank’s risk profile changes; MAS specifically frames these controls as lifecycle practices that should evolve as adoption develops.
10. Assess resilience, subcontractors and concentration
Obtain business-continuity and disaster-recovery evidence, including recovery time and recovery point objectives, test results, capacity assumptions and communications during an outage. Understand whether the platform depends on a separate model provider, cloud region, data-labeling service, observability tool or identity system. Map subcontractor names, functions, locations, access and notification rights.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallEvaluate concentration at the portfolio level. Several apparently separate use cases may rely on the same model, cloud, retrieval service or provider control plane. Decide which activities can share a dependency and which require an alternative path. The exit plan should identify a replacement service or manual process, exportable records and prompts, data deletion steps, transition support, contract termination triggers and a tested continuity period.
11. Avoid common evaluation mistakes
- Starting with a vendor feature list: scope authority and harm first; otherwise the most capable demo wins by default.
- Treating certification as suitability: assurance reports support diligence but do not prove performance for your workflow or jurisdiction.
- Assuming “human in the loop” is sufficient: define reviewer authority, information, response time, segregation and override behavior.
- Ignoring non-model components: retrieval, tools, credentials, logs and integrations can create the material failure.
- Skipping exit testing: an export clause is not a practical replacement if data, workflows or approvals are proprietary.
- Publishing unverified market statistics: an EBA 2025 factsheet search excerpt mentions a 55% adoption figure, but the underlying PDF wording, sample and definition were not verified; do not use that number as evidence until the source is checked directly.
12. Make the decision and keep it reviewable
Choose the platform that meets the defined use with the fewest unresolved high-impact exceptions, not the one with the longest feature list. Your decision record should state intended use, authority, data boundary, test results, provider evidence, residual risks, compensating controls, approval owners, monitoring plan, change triggers and exit route. Revisit that record on a schedule tied to materiality and whenever the agent, provider, connected systems or applicable supervisory expectations change.
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