Agentic AI vs Generative AI: A Financial Services Leader's Guide

Agentic AI and generative AI are used interchangeably across financial services, but they solve different problems and carry different regulatory weight. This guide defines them, explains how regulators treat them differently, and provides a practical framework for the respective type (of AI) in each financial services workflow.

Introduction

In April 2026, US banking regulators updated their model risk guidance and marked a clear distinction that many vendors are still confused about. The Federal Reserve's SR 26-2 and the OCC's matching Bulletin 2026-13 both state that generative and agentic AI are not currently covered by current model risk frameworks [1][4]. They are seen as different enough from traditional models to require their own approach respectively. This is a regulator confirming, in effect, that these two technologies are not the same.

Most financial institutions no longer question whether to use AI. Instead, they focus on which type to use for specific tasks. Mixing up these two aspects can result in poor build decisions, inappropriate governance models, and wasted budgets on unnecessary tools.

The guide gives clear definitions of both terms, tailored to the financial services sector; it explains why this distinction matters for regulators and provides a practical framework for deciding what technology a given workflow needs. It also outlines how Dailoqa's own enterprise-grade Broccoli™ platform measures up against both.

What is Generative AI in Financial Services?

Generative AI produces one output., This can include a summary, a draft, or an answer, on the basis of a prompt and then ends the interaction there. In the financial services sector, this might mean preparing a credit memo from structured input, summarising a long filing, or responding to a policy question using an internal knowledge base.

Gartner's 2026 hype cycle work on agentic AI clearly distinguishes between the two: generative AI creates content, while agentic AI takes action [2].

The practical limit is that generative AI cannot carry out a task on its own. Someone must read the draft memo, decide if it is correct, and move it to the next step. This approach works well for tasks that require considerable judgment and produce a single output. However, it becomes a bottleneck for tasks that require many steps or involve many records.

What is Agentic AI in Financial Services?

Agentic AI creates a series of steps to reach a goal. It carries them out using tools and data sources, adjusting along the way without needing someone to prompt each step. In financial services, this means conducting a complete KYC check from start to finish, reconciling transactions across different systems, or managing a document queue and marking exceptions for a human reviewer.

Google Cloud's 2025 survey of 556 financial services leaders found that institutions are already moving agentic AI out of the pilot stage and into live production for this type of multi-step operational work [3]. The real difference with generative AI isn't how advanced the underlying model is. It is about whether the system creates an output or performs a process.

Generative AI vs Agentic AI vs Broccoli™: A Comparison

Dailoqa has created the Broccoli platform that offers agentic AI capability but with auditability, compliance guard-rails and specific to financial services. The table below shows where each fits and how Broccoli™ applies the difference in practice.

Dimension	Generative AI	Agentic AI	Broccoli™ Agentic AI platform
Input	Single prompt	A goal or trigger event	A goal, trigger event, or regulatory rule
Output	One piece of content	A completed multi-step process	A completed, auditable process with a documented decision trail
Autonomy	None beyond the single response	Multi-step, tool-using	Multi-step, tool-using, bounded by encoded compliance rules
Human involvement	Reviews and approves the output	Sets the goal, reviews exceptions	Owns judgment on flagged decisions while agents handle the rest
Typical FS task	Drafting, summarising, answering	KYC, reconciliation, document processing	The agentic ai built with compliance and governance for regulated financial services from the ground up

Enterprise Use Cases: Where Each Type Delivers the Most Business Value

The framework above establishes which technology fits which workflow. The next question is where that choice shows up in the numbers.

The distinction is simple. Generative AI produces value by improving the quality and speed of a single deliverable. Agentic AI produces value by removing the handoffs between deliverables. One raises output quality; the other changes the unit economics of the process itself.

Retail and commercial banking:

Generative AI drafts the loan decision summary, the adverse action notice, and the branch response to a customer query. Agentic AI runs the origination journey itself: document intake, KYC checks, bureau pulls, income verification, and exception flagging across the loan origination system. The value sits in straight-through processing rates, not in drafting time.

Wealth management:

Generative AI prepares the portfolio review pack ahead of a client meeting. Agentic AI monitors portfolios

continuously against mandate, suitability, and rebalancing triggers, and raises a case when a threshold is breached. The first saves an adviser an hour. The second changes how many clients an adviser can hold.

Risk and compliance:

Generative AI answers a policy interpretation question against internal documentation. Agentic AI works the alert queue: gathering evidence across systems, building the case file, closing what is clearly benign, and escalating what is not. Alert volumes have grown faster than compliance headcount at most institutions, and only the second approach addresses that.

Capital markets:

Generative AI drafts the trade rationale note. Agentic AI reconciles trades across venues, identifies breaks, and prepares the exception pack for the desk. Reconciliation is multi-step, high-volume, and rules-bound, which is precisely the profile agentic AI is suited to.

Insurance:

Generative AI summarises a claims file for an adjuster. Agentic AI runs first notice of loss end to end, from intake through validation to triage, and hands the adjuster only the cases that require judgment.

Google Cloud's 2025 survey of financial services leaders found institutions moving agentic AI out of pilots and into live production specifically for this category of multi-step operational work [3]. The pattern in Dailoqa's own deployments is consistent with that: generative AI improves individual outputs, while the 40% reduction in operating costs we see in production comes from agentic systems running whole processes.

Why Regulators Are Already Drawing This Line

The Federal Reserve's SR 26-2, issued in April 2026, updates guidance on managing model risk. It notes that generative and agentic AI are not included in its current scope, pending further guidance [1]. The OCC issued matching guidance the same month as Bulletin 2026-13 [4]. Neither regulator claims these technologies are unregulated. They argue that the current model risk framework, which is designed for static and testable models, does not accommodate a system that reasons and acts in multiple steps, and that a different approach is needed.

American Banker's analysis of the guidance shows that this creates a real gap for banks. Institutions using agentic AI must develop their own governance strategy before formal rules are established, not afterwards [5]. That gap is where the difference between generative and agentic AI shifts from definitions to function. A generative AI drafting tool and an agentic AI system making decisions have different risk profiles. Treating them the same in a governance framework overlooks where the real risks lie.

“Treating agentic AI and generative AI as the same technology is where most institutions get their architecture completely wrong. One produces just an answer. The other is executing a workflow. Build governance for the wrong one, and the real risk goes unmanaged.”

Jeet Parekh, Chief AI Architect, Dailoqa

Which One Does a Given Workflow Actually Need?

Not every task requires independence, and not every task should end with just one result. Here is a simple way to look at it. If the work involves a lot of judgment and leads to one deliverable, generative AI is appropriate. If the work is repetitive, involves multiple steps, and connects different systems, agentic AI is suitable. Most organisations need both types, used in different parts of the same process.

Vertical	Task	Recommended technology
Retail & Commercial Banking	Drafting a loan decision summary	Generative AI
Retail & Commercial Banking	End-to-end onboarding and KYC	Agentic AI
Wealth Management	Summarising a portfolio review for a client meeting	Generative AI
Wealth Management	Ongoing portfolio monitoring and rebalancing triggers	Agentic AI
Risk & Compliance	Answering a policy interpretation question	Generative AI
Risk & Compliance	Continuous transaction monitoring and case building	Agentic AI
Capital Markets	Drafting a trade rationale note	Generative AI
Capital Markets	Multi-step trade reconciliation across venues	Agentic AI
Insurance	Summarising a claims file	Generative AI
Insurance	End-to-end first notice of loss processing	Agentic AI

Agentic AI vs Generative AI: Key Pros and Cons for Enterprise Adoption

Both technologies have a place in a financial institution, and most institutions will run them alongside each other. Neither is straightforward to adopt. The trade-offs are different in kind, not just in degree.

Generative AI		Agentic AI
Pros	Cons	Pros	Cons
Produces credit memos, client summaries, and policy answers in minutes rather than hours	Cannot progress a case; every output waits for a person to move it forward	Runs a case end to end, from intake and checks through to exception flagging	Errors compound across steps before a human sees the result
Sits on top of existing systems with no process re-engineering required	Value is capped by reviewer capacity, so it does not scale with volume	Scales with transaction volume rather than reviewer capacity, lifting STP rates	Governance is harder, because you are supervising a process rather than an output
Straightforward to govern: one prompt, one output, one accountable reviewer	Hallucination risk is carried entirely by the person reviewing the draft	Standardises how a process is executed across branches, regions, and channels	Requires clean tool and data access; legacy integration is the real cost line
Inexpensive to pilot and inexpensive to withdraw if it underperforms	Weak on tasks requiring live data drawn from multiple systems	Produces an auditable decision trail as a by-product of doing the work	Every autonomous decision boundary needs a named, accountable owner
Raises the floor on written quality across a large and uneven team	Savings appear as time saved, which rarely converts into a cost line	Frees skilled staff for judgment on risk appetite, suitability, and exposure	Higher upfront cost and an architectural commitment, not a pilot

Most of those drawbacks are architecture problems rather than model problems. Error propagation, supervision, integration, and accountability are all design decisions taken before a single agent goes live, which is why generic agentic tooling struggles in a regulated environment: it treats them as configuration.

This is the gap the enterprise-grade Broccoli™ platform was built to close. Compliance and policy requirements are encoded as boundaries the agents operate within, not as guidance they are asked to follow. The decision trail is documented as the process runs, so evidence exists before the regulator asks for it. Judgment-heavy steps route to a named human owner by design. And the principle of minimum effective intelligence applies throughout  rules where rules will do, machine learning where patterns matter, large language models only where reasoning is genuinely required, and human oversight where the decision carries risk. The result is a lower error surface and a materially lower cost of governance than a general-purpose agentic stack.

Combined Intelligence in Practice

Dailoqa’s work is underpinned by Combined Intelligence. This means that agents handle document intake, data reconciliation, and first-pass checks. Humans make the final decisions on anything related to risk appetite, suitability, or reputational exposure, with a named, accountable decision-maker always responsible. Generative AI assists by drafting and summarising according to guidelines set by rules and financial expertise.

Frequently Asked Questions on Agentic AI Vs Genrative AI

What is the difference between agentic AI and generative AI?

Generative AI, when given a prompt, creates just one output, for example a draft or a summary. Agentic AI, on the other hand, plans and carries out several steps towards a goal by making use of tools and data throughout the process, without requiring a person to provide a prompt for each step.

Is agentic AI just generative AI with extra steps?

No. Agentic AI typically makes use of a large language model as the basis for its reasoning, but includes features such as planning, the use of tools, and independent execution which generative AI does not carry out.

Do banks need both generative AI and agentic AI?

The same institutions have different requirements depending on the task in question. Generative AI is well suited to tasks which involve a single output and require a high degree of judgment, while agentic AI is more appropriate for repetitive, multi-step processes that involve a number of systems.

Is agentic AI regulated the same way as traditional AI models?

It is not but the regulators have strongly indicated that they will be treated differently. The guidance issued by US banking regulators in April 2026 excludes generative and agentic AI from the current model risk frameworks, with more specific guidance for these technologies to be provided later.

What financial services tasks is agentic AI best suited for?

It is best for multi-step, high-volume operational work like KYC, transaction reconciliation, and document processing, where a process needs to run from start to finish rather than produce a single output.

How does Combined Intelligence relate to agentic AI?

Combined Intelligence is Dailoqa's model for using agentic AI in financial services. Agents carry out the process while humans keep ownership of judgment-heavy decisions, and rules encode regulatory and policy requirements.

What is the Broccoli™ agentic AI platform?

It is Dailoqa's platform for applying agentic AI in regulated financial services, designed to maintain a documented, auditable decision trail and ensure human oversight on judgment-heavy steps.

Should compliance teams treat agentic AI as higher risk than generative AI?

Yes, the risk level is much higher with agentic AI than with generative AI. Agentic AI can act independently at various stages. Errors can accumulate before a human looks at them. Organisations must design oversight with agentic AIas compared to single-output generative AI responses.

Conclusion

The distinction between agentic and generative AI became significant when regulators included it in their guidance. Organizations which use these terms in the same way are making decisions about development and governance without clearly defined boundaries, thereby increasing associated risks.

The enterprise-grade Broccoli™ platform serves as the agentic layer for regulated financial services. If your institution is figuring out which decisions should remain human and which processes can handle agentic AI, this is a discussion worth having before the next development decision, not after.

Talk to Dailoqa about an AI readiness assessment.

Glossary

Generative AI: It is an AI model which, in response to one prompt, generates new content, such as text, code, or a summary and then stops. It will not carry out any action beyond producing that output.

Agentic AI: An AI system capable of planning, carrying out, and making adjustments over a number of steps in order to achieve a goal, using various tools and data sources throughout the process, without it being necessary for a person to prompt each step.

Large language model (LLM): The underlying model that is the reasoning engine in both generative and agentic systems. It is not a category on its own.

Model risk management: The discipline of identifying, measuring, and controlling the risk that a model produces an incorrect or harmful output. It has historically focused on static, testable models.

Combined Intelligence: Combined Intelligence is Dailoqa's model for using agentic AI in financial services. Agents carry out the process while humans keep ownership of judgment-heavy decisions, and rules encode regulatory and policy requirements.

Enterprise-grade Broccoli™ platform: It is Dailoqa's platform for deploying agentic AI within the regulated financial services sector., Broccoli puts Combined Intelligence into practice by maintaining a documented and auditable record of decisions and by providing human oversight for the steps that involve a high degree of judgment. It is then referred to later in this guide as a point of direct comparison with generic generative and agentic AI.

References

[1] Federal Reserve. "SR 26-2: Guidance on Model Risk Management for Generative and Agentic AI." 2026. https://www.federalreserve.gov/supervisionreg/srletters/SR2602.htm

[2] Gartner. "Hype Cycle for Agentic AI, 2026." 2026. https://www.gartner.com/en/articles/hype-cycle-for-agentic-ai

[3] Google Cloud. "New Research Shows How AI Agents Are Driving Value for Financial Services." 2025. https://cloud.google.com/transform/new-research-shows-how-ai-agents-are-driving-value-for-financial-services

[4] Office of the Comptroller of the Currency. "OCC Bulletin 2026-13." 2026. https://www.occ.gov/news-issuances/bulletins/2026/bulletin-2026-13.html

[5] American Banker. "Regulators' Guidance on Model Risk Leaves Many Questions Unanswered." 2026. https://www.americanbanker.com/opinion/regulators-guidance-on-model-risk-leaves-many-questions-unanswered