Artificial intelligence has quickly moved from abstract discussion to operational reality. The newest development—“agentic” AI—goes beyond chat interfaces and predictive analytics. These systems are designed to initiate, plan, and carry out multi-step tasks on their own, such as monitoring regulatory updates, assembling compliance files, or even engaging with customers directly.

Agentic AI presents both promise and peril for community banks. Properly implemented, these systems can help institutions manage regulatory obligations, reduce administrative costs, and enhance customer service. But the very autonomy that makes them valuable also creates heightened compliance risk.

Our advice to clients is clear. Approach agentic AI as you would any high-impact technology deployment—through the lenses of model risk, consumer protection, data security, and operational resilience. Establish governance up front, start with low-risk pilots, and maintain human accountability at every step. By embedding these controls, community banks can innovate responsibly while remaining well-positioned for regulatory scrutiny.

Below we outline practical applications (i.e. internal use cases), the compliance implications of each, and the frameworks and safeguards we recommend our clients consider.

Internal Use Cases

Regulatory Monitoring and Policy Updates

Agentic systems can monitor federal and state regulators, flag developments, and even draft suggested policy revisions. Agents can be tasked to ingest and summarize changes to the Federal Register and state equivalents, regulatory guidance and case law developments, and draft updates to institution policies and procedures to reflect these changes.

Banks utilizing agents for these tasks should take into account:

• Compliance Issues: Avoid over-relying on AI summaries; be wary of inadvertent distribution of incomplete or inaccurate interpretations; monitor the lack of source documentation.
• Safeguards: Require human approval of all proposed policy changes; maintain links to source material; log all outputs.
• Frameworks: Follow National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF) and traditional model risk management guidance, such as the Federal Reserve’s and the Office of the Comptroller of the Currency’s supervisory guidance on model risk management (SR 11-7 and OCC 2011-12, respectively).

BSA/AML Case Management

An agent may collect know-your-customer documentation, draft narratives, or assemble suspicious activity reports.

• Compliance Issues: Review how the agents explain decisions and handle sensitive personal data under the Gramm-Leach-Bliley Act, Regulation P and the Interagency Guidelines Establishing Standards for Safeguarding Customer Information; watch for potential bias in triage.
• Safeguards: Use strict role-based access controls, immutable audit logs and human sign-off on suspicious activity reports and currency transaction reports filings.
• Frameworks: Meet Bank Secrecy Act/AML Manual expectations, NIST 800-53 for logging and access controls.

Credit Underwriting Support

Agents can draft credit memos, monitor covenants, and propose early-warning actions.

• Compliance Issues: Review the agent’s memos for fair lending compliance and disparate impact under the Equal Credit Opportunity Act and Regulation B. Use prompting to improve the agent’s ability to provide adverse action reasons.
• Safeguards: keep agent outputs advisory; require documented feature importance; test periodically for bias.
• Frameworks: follow SR 11-7 / OCC 2011-12 model validation, fair lending governance.

Fraud and Dispute Triage

Agents may open cases, draft customer notices, and track Reg E timelines.

• Compliance Issues: Check for compliance with Regulation E deadlines, risk of misleading communications and exposure to Unfair, Deceptive or Abusive Acts and Practices (UDAAP).
• Safeguards: Use hard-coded timing rules and templates for disclosures. Oversee exceptions.

Customer-Facing Use Cases

Conversational Banking Copilots

Customer-facing agents can answer FAQs, assist with transfers, and schedule branch visits.

• Compliance Issues: Confirm the agents are satisfying regulatory requirements: identity verification, compliance with E-SIGN and Regulation E; accessibility under the Americans with Disabilities Act and applicable web content accessibility guidelines.
• Safeguards: Step-up authentication for transactions. Use confirmation prompts, bilingual and accessible interfaces.

Smart Onboarding and Pre-Qualification

Agents can walk applicants through account opening, gather documents, and propose products.

• Compliance Issues: Review the agent’s activities, recommendations and responses for fair lending concerns in product steering; adverse action notice requirements under the Fair Credit Reporting Act, Equal Credit Opportunity Act and Regulations V and B.
• Safeguards: Use standardized eligibility criteria; log and disclose adverse action reasons; obtain clear consent for data use.

Small Business Cash-Flow Copilots

Agents may forecast cash gaps, propose draws on credit lines, or generate reminders.

• Compliance Issues: Review agent responses for accuracy and structure to avoid suitability and UDAAP risk if forecasts are framed as advice; authentication for linked accounts.
• Safeguards: Label outputs as “educational” or “informational;” require opt-in and explicit consent for data sharing.

Collections Engagement

Agents can schedule outreach, propose hardship plans, and send messages.

• Compliance Issues: Ensure compliance with Fair Debt Collection Practices Act-like restrictions on timing and content; risk of unfair treatment.
• Safeguards: Enforce call-time restrictions and use pre-approved message libraries.

Cross-Cutting Issues

Model Risk and Explainability

Treat agentic AI as a composite “model” subject to SR 11-7 and OCC 2011-12. Maintain model inventory, document assumptions, and provide reason codes for outputs.

Levels of Autonomy

Define in advance what actions an agent may take:

• Advisory only
• Draft plus human approval
• Limited execution with dual control or
• Full execution in rare circumstances.

Data Privacy and Security

Minimize personally identifiable information in prompts. Use retrieval-augmented methods to control scope. Enforce encryption, retention schedules and defensible deletion.

Fairness and Consumer Protection

Test for model bias and disparate outcomes before and during deployment. Ensure clarity between “advice” and “education.” Avoid dark patterns in customer interfaces.

Third-Party Risk

Evaluate model providers, orchestration platforms and data hosts under federal regulators’ Interagency Guidance on Third-Party Relationships: Risk Management. Secure appropriate contractual assurances, such as covenants, representations and warranties around AI trustworthiness and compliance with law, strong indemnification and exist strategies for breach.

Operational Resilience

Design fallback procedures to ensure that if AI services fail, human workflows remain functional. Rate-limit automated tasks to prevent overload.

Implementation Blueprint

• Governance First: Establish an AI risk committee. Adopt NIST AI RMF. Create an “agent action catalog” with limits and required approvals.
• Low-Risk Pilots: Begin with regulatory monitoring or customer FAQs—functions where errors carry less consumer harm. Require human review of outputs.
• Scaling with Controls: Expand cautiously into BSA case support or Reg E dispute tracking. Introduce autonomy only where hard limits and human oversight exist.
• Continuous Improvement: Conduct periodic fairness testing, independent validation and examiner-ready reporting.

Practical Takeaways for Boards and Management

• Document the governance framework and inventory every AI tool.
• Define autonomy levels clearly.
• Implement least-privilege security and immutable logging.
• Integrate AI outputs into existing records-retention and e-discovery programs.
• Conduct fairness and UDAAP reviews regularly.
• Prepare examiner-ready evidence packages linking each safeguard to applicable regulations.

Should you have any questions regarding how your bank may deploy AI agents, please contact Christopher Couch or a member of the Phelps Banking and Financial Services team.