For compliance officers and risk managers at financial institutions, the ambiguity surrounding the difference between “AI agents” and “LLMs” has real consequences. Governance for a language model differs fundamentally from those required for an autonomous system that reads documents, queries databases, and takes actions on behalf of the organisation.
Understanding the distinction matters practically as it determines how your model risk management program scopes validation, what your audit trail requirements look like, and whether a given AI deployment falls under existing model risk guidance or represents new territory.
This article explains what LLMs and AI agents are architecturally, how they differ in practice, and what those differences mean for financial institutions.
What Is a Large Language Model (LLM)?
A large language model is a deep learning system trained on massive text corpora to understand and generate language. At its core, an LLM takes a text prompt as input and predicts the most probable next tokens, producing a response.
Without additional scaffolding, an LLM does not initiate actions, maintain state between conversations, or access external systems. GPT-4, Claude, Gemini, and Llama are all LLMs: powerful reasoning and language systems that respond to inputs rather than pursue goals.
What LLMs Are Good At
LLMs excel at tasks involving language understanding and generation within a single interaction, such as:
- Summarizing documents
- Drafting communications
- Classifying text
- Answering questions against provided context
- Extracting structured data from unstructured sources
For compliance teams, this translates to practical tools:
- Regulatory document summarization
- Policy gap analysis
- First-pass review of examination materials
The key constraint is the single-turn nature of a bare LLM: without external tools or persistent memory, it can reason and generate but cannot retrieve, verify, or act.
What Is an AI Agent?
An AI agent is a software system that uses an LLM as its reasoning core and adds a layer of orchestration on top:
- Planning
- Memory
- Tool use
- Action execution
The agent loop typically follows this sequence:
- Perceive the current state
- Reason using the LLM to decide what to do next
- Select and call a tool (an API, a database, a code executor)
- Observe the result, and repeat until the goal is achieved
This loop distinguishes agentic AI from a simple LLM call. The system runs for multiple cycles, taking real-world actions at each step.
What AI Agents Are Good At
Agents suit workflows requiring more than one step and involving external data or actions. For financial institutions, examples include:
- Continuously monitoring regulatory publication feeds for policy-relevant changes
- Conducting multi-source research by querying document repositories and external databases
- Executing end-to-end workflows like drafting a regulatory response that requires pulling case history, cross-referencing policy documents, and formatting the output
Key Differences: AI Agent vs. LLM
Six attributes matter most when practitioners evaluate these systems for financial institution deployments.
| Attribute | LLM | AI Agent |
|---|---|---|
| Core function | Generate language in response to a prompt | Plan and execute multi-step tasks toward a goal |
| Autonomy level | None (human initiates and interprets each call) | Variable (can run multiple cycles with minimal human input) |
| Memory | Stateless by default; context window only | Persistent memory via vector databases or episodic logs |
| Tool access | None in a base LLM | Built-in tool use: APIs, databases, code execution |
| Task scope | Single-turn, single-task | Multi-turn, multi-task, goal-directed |
| Risk profile | Bounded by quality of generated text | Extends to actions taken in connected systems |
The most consequential distinction for governance is the last row: risk profile. An LLM that generates an inaccurate summary creates a quality problem. An AI agent connected to a compliance management system that acts on inaccurate reasoning creates a compliance problem.
Risks and Compliance Considerations for Financial Institutions
AI agents introduce a risk profile that goes beyond what compliance teams typically encounter with bare LLMs. Autonomous action, multi-step execution, and connections to external systems each add governance dimensions that require explicit treatment.
The regulatory framework for AI in banking is actively evolving. In April 2026, the Federal Reserve, OCC, and FDIC issued revised interagency model risk management guidance designated as SR 26-2, which supersedes the long-standing SR 11-7 guidance. The agencies have indicated they plan to issue separate guidance addressing AI systems, including agentic AI, and have called on institutions to apply their broader risk management and governance practices to agentic AI in the interim.
Organizations deploying AI agents must apply existing enterprise risk management frameworks to these systems and document this process. The FFIEC IT Examination Handbook continues to cover information systems governance, which encompasses the external connections and data flows that agentic systems depend on.
Four aspects to be addressed before deploying an AI agent at a financial institution:
- What is the agent explicitly authorized to do, and which systems can it read from and write to?
- An agent contains at minimum two models: the LLM and the orchestration logic. Institutions should validate both layers and document the interaction.
- Agents that execute multi-step workflows need logs showing each step: what the agent perceived, what it reasoned, which tool it called, what the tool returned, and what action it took.
- Can the agent be overridden or paused? Institutions should define the conditions under which an agent’s workflow is escalated to a human reviewer.
Frequently Asked Questions
Can an LLM work without an AI agent framework?
Yes. LLMs are useful on their own for single-turn tasks: summarization, classification, drafting, and question answering against provided context. An agent framework is needed only when the task requires multi-step execution, persistent memory, or actions in external systems
What makes an AI agent autonomous?
Autonomy in an AI agent refers to the system’s ability to run multiple reasoning and action cycles without requiring human input at each step. An autonomous agent perceives its environment, reasons using an LLM, selects and executes a tool, observes the result, and loops until the task is complete. The degree of autonomy varies. Some agents require human approval before taking actions while others execute end-to-end.
How does SR 26-2 apply to AI agents?
The interagency revised model risk management guidance issued in April 2026 (SR 26-2) explicitly excludes generative AI and agentic AI from its scope, noting that separate guidance is forthcoming. The Federal Reserve, OCC, and FDIC expect institutions to apply their broader enterprise risk management and governance practices to agentic AI in the interim. Institutions deploying agents in production should document their governance approach and be prepared for heightened examiner scrutiny as dedicated regulatory guidance develops.
What is the difference between agentic AI and a traditional LLM?
A traditional LLM produces a text output from a single prompt and stops. Agentic AI uses an LLM as its reasoning core but adds the ability to take actions, call tools, retain memory across steps, and pursue a goal over multiple cycles.
Before deploying any AI system at a financial institution, establish whether it is a language model responding to prompts or an agent executing goals. That determination shapes every governance decision that follows, from model risk management scope to the design of human review checkpoints.
Discover AI-powered technology that helps manage every aspect of risk and compliance, all in one platform.
Request Demo- Risk Prediction
- Regulatory Tracking
- Workflow Automation
- Integrated GRC