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Trust in agentic AI: How the technology can earn confidence

Mary Ellen Biery
October 13, 2026
0 min read

A Gartner report explores the dynamic trust required for agentic banking

Financial institutions can build trust in agentic AI for lending by defining roles, limiting actions, escalating uncertainty, preserving human review, and monitoring results.

This article covers these key topics: 

Earning trust continuously: A requirement for agentic banking to scale

Trust is a guiding principle for financial institutions and a key expectation among their customers, so as artificial intelligence (AI) takes on more banking work, institutions need to consider how they’ll assess and maintain trust in those systems.

In “Dynamic Trust is a New Banking Differentiator,”* Gartner author Benjamin Seesel argues that for agentic banking to scale, it will require that trust be assessed on an ongoing basis. “In a traditional bank, trust was static, granted until an event-triggered review,” Seesel writes. “In the agentic bank, trust will be dynamic. The bank will need to earn trust continuously, in real time.”

Read the Gartner report on building trust in agentic AI.

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The report describes some of the capabilities banking Chief Information Officers (CIOs) should consider as they evaluate that shift, and it encourages CIOs to prioritize investments in emerging technologies that build dynamic trust.

The Gartner report does not evaluate Abrigo or other technology vendors, nor does it endorse specific agentic banking solutions, such as Abrigo APX for Lending. However, trust in agentic AI is easier to discuss when the conversation gets specific. This post applies selected ideas from the report to lending workflows from Abrigo’s perspective.

Banks and credit unions can build trust in agentic AI for lending by defining roles, limiting actions, escalating uncertainty, preserving human review, and monitoring results.  

Define each agent’s role and context

The Gartner report discusses the importance of banks and credit unions defining what AI systems can access and rely on, establishing “a governed version of enterprise truth.”

In lending, that capability starts with the agent’s assigned work and the information available to it. Abrigo’s APX for Lending, for example, assigns each agent a specific job based on an institution’s policies and procedures. An intake agent checks an application for required documents and accurate information.

Nemalikanti

A policy exceptions agent compares a deal with the financial institution’s policies. APX for Lending agents operate with institution-authorized data, source documents, workflow context, role-based permissions, institution-specific policies and configurations, and approved integrations. The platform controls access to tools and maintains tenant-specific data boundaries.

“The platform itself can break everything down into steps, act across the entire workflow, use the institution's data and the policies and the permissions, and pull all of that together into a context layer that will help the agents—that are again, very role-specific agents—move things between them,” said Abrigo’s Chief Product and Technology Officer Ravi Nemalikanti.

Question for CIOs: What does each agent need to see, and who will review its access and workflow settings?

Set boundaries before an agent acts

The report discusses coordinating how AI-enabled decisions are made, escalated, and carried out. In lending, financial institutions can set autonomy by workflow, action type, role, loan type, confidence threshold, and risk level.

For example, an intake agent might identify a missing document or a tax return for the wrong year and draft a request for the borrower. But perhaps a specific bank or credit union requires a staff member to physically send the email. “Some organizations don't want to have communications with borrowers be driven by AI, which is fine,” Nemalikanti said. “We appreciate that, and that's a specific opt-in” for Abrigo’s agentic lending solution.

In other words, actions can be approved, deferred to a person, or even blocked, based on the institution’s specific requirements.

While a message awaits staff review or a borrower response, the intake agent may continue other permitted work, such as reviewing additional application documents, extracting data, or flagging inconsistencies. This can let other parts of the workflow continue while one item awaits attention—a major difference from rules-based AI, where all activity would stop when work hits an exception.

Question for CIOs: Which actions may an agent take on its own in our workflows, and which should require staff approval?

Plan for exceptions and uncertainty

An agent’s response to a difficult case matters as much as its handling of a routine one. The Gartner report notes that agentic systems can act at scale, which makes control and escalation important considerations for banks and credit unions.

In agentic lending workflows, financial institutions can translate their risk appetite and credit policy into structured requirements. Those could take the form of thresholds, required evidence for a particular decision, approval authorities, prohibited loan types, or restricted industries. Nemalikanti said exception paths that require human review should be versioned, tested, and controlled by the institution with the provider’s assistance. “You do not create reliable guardrails by just uploading a policy PDF and hoping that the platform got it right and got it right every single time,” he said.

Question for CIOs: What conditions should stop an agent from proceeding, and what evidence and context must a reviewer see to resolve exceptions?

Keep people involved where judgment matters

Gartner describes human oversight as part of the progression toward more agentic banking.

In lending, a workflow may need to be reviewed when data is missing, evidence cannot be verified, confidence is insufficient, permissions are missing, configuration is invalid, or a system component is unavailable.

Abrigo notes that APX for Lending grounds agent recommendations in evidence and allows institutions to route low-confidence or higher-risk work to human review. For example, if a closing package audit agent identifies a “hard stop” condition, it will flag the deal for a person and provide details about what it found and why the issue needs attention.

Question for CIOs: Which lending decisions require human judgment, and who has the authority to resolve an escalation?

Make agent activity reviewable

Continuous trust of AI-enabled systems will require that banking CIOs are able to show “what an agent saw, what it decided, what it executed, which policy it applied and when a human intervened,” according to the Gartner report.

In lending, that may mean recording an action’s trigger, evidence, reasoning, confidence, applicable policy, reviewer activity, and outcome.

Abrigo APX for Lending can show what a financial institution wants the human reviewer to see when review is triggered, including an agent’s recommendation, supporting evidence, confidence information, applicable policy context, and reason for escalation. Staff then can approve, reject, escalate, or request a revision, with their decisions and notes captured in the audit trail.

When information is incomplete or rules conflict, an agent should identify the gap and escalate rather than fill it with unsupported information that can contribute to hallucinations. “At the end of the day, the credit decision is with the institution,” so the institution needs human oversight in the areas it deems necessary, Nemalikanti said.

Question for CIOs: Can staff reconstruct an agent’s action and the evidence and policy that informed it?

Reassess trust as agents change

Trust requires attention after deployment, too. The Gartner report calls for continuous monitoring and feedback, as well as ongoing verification, as part of maintaining trust in AI-enabled systems.

For agentic lending workflows, institutions may evaluate agents before release, monitor acceptance, modification, rejection, and override patterns, and periodically review agent performance and governance settings. Those reviews can inform whether an agent’s role should expand, contract, or pause. Abrigo believes that governed agentic execution depends on both platform controls and institutional oversight.

Question for CIOs: How will your institution assess agent changes before expanding their role, and who will review performance to govern autonomy?

More resources for evaluating agentic AI

The Gartner report gives CIOs a way to frame the trust conversation around agentic banking, and it explains how and why the type of trust in agentic banking is different from trust tied to legacy banking.

But financial institutions have many resources on preparing for, evaluating, and implementing agentic AI technology.

The Conference of State Bank Supervisors released an AI Supervisory Framework on Sept. 16, 2026. The framework is a discretionary tool for state examiners to identify AI use and assess related risks. Financial institutions can also use it to assess their AI programs and understand the general approach, types of questions, and the information that a state examiner may request regarding the institution’s AI-based products, services, and tools. Each state will decide how much to incorporate into its supervision.

Abrigo also has multiple resources to help institutions understand trust and governance considerations and to review vendors under consideration. The blogs, whitepapers, and guides include information on SR 26-2, the revised Interagency Guidance on Model Risk Management, an AI governance vendor questionnaire, a board member’s guide on governance basics for community financial institutions, and an on-demand webinar on how to operationalize AI safely and effectively.

*Gartner, Agentic Banking Requires a New Type of Trust, Benjamin Seesel, 13 July 2026.

Gartner is a trademark of Gartner, Inc. and/or its affiliates.

Explore governed AI agents for community bank and credit union lending.

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FAQs

What does dynamic trust mean for agentic banking?

The Gartner report says that institutions will need to assess trust in AI-enabled systems continuously as they operate and take on more work. In Abrigo’s opinion, institutions looking at agentic lending can do this by focusing on agent roles, action limits, escalation, human review, reviewable activity, and ongoing monitoring.

What does an AI agent do in a lending workflow?

An agent can perform a defined task, such as checking an application for missing documents, extracting information or comparing a deal with institutional policies. Its role and access should reflect the institution’s workflow and requirements.

What controls help build trust in agentic AI for lending?

Institutions can define each agent’s role and data access, set action boundaries, establish exception and escalation rules, involve staff where judgment is needed, keep an audit trail, and monitor performance.

When should an AI agent escalate a lending task to a person?

An agent should escalate when information is missing, evidence cannot be verified, confidence is insufficient, rules conflict, permissions are missing, or a configured stop condition is met. Reviewers need enough evidence and context to resolve the issue.

What should a bank or credit union review after deploying AI agents?

A bank can review agent performance, governance settings, staff acceptance, modifications, rejections, and overrides. Those findings can inform next steps and whether those include maintaining, expanding, reducing, or suspending an agent’s role.

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The information, content and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.

About the Author

Mary Ellen Biery

Senior Strategist & Content Manager
Mary Ellen Biery is Senior Strategist & Content Manager at Abrigo, where she combines financial journalism, original research, and SEO/AEO strategy to create authoritative content that helps financial institutions manage risk and pursue growth. A former Dow Jones Newswires equities reporter, her work has appeared in The Wall Street Journal,

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About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

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