By 2028, Gartner expects a third of enterprise software applications to perform that kind of multi-step work through agentic AI, up from less than 1% in 2024. For risk and compliance leaders, the question is shifting from whether autonomous software belongs in the program to how to choose the framework that will run it responsibly.
The framework you select becomes part of your control environment, and examiners will expect the same accountability from an autonomous agent that they expect from a junior analyst. This guide explains what an agentic AI framework is, how it differs from the generative tools you already use, and the criteria that matter.
Also see our complimentary whitepaper to learn more about agentic AI for financial institutions.
What Is an Agentic AI Framework?
An agentic AI framework is the software scaffolding used to build, run, and supervise AI agents. To understand what agentic AI is in practical terms, separate two ideas:
- The agent is the autonomous actor: it takes a goal, breaks it into steps, decides what to do next, and acts.
- The framework is the surrounding infrastructure that gives the agent memory, connects it to tools and data, coordinates multiple agents, and lets a human watch and intervene.
The agent reads its inputs, reasons about how to reach the goal, selects a tool or action, executes it, observes the result, and repeats until the task is done or a stopping condition is met. A single agent might pull a regulation, compare it to existing policy, and propose an edit. A multi-agent setup might split that work across a researcher, a drafter, and a reviewer.
Frameworks such as LangGraph, CrewAI, and Microsoft’s AutoGen, each provide orchestration, state management, and tool integration so developers do not rebuild those mechanics for every project. For a risk program, the framework is also where oversight lives. The controls you can apply to an agent are only as strong as the controls the framework exposes.
The Core Components Every Agentic AI Framework Provides
Underneath the marketing, an AI agent framework offers four capabilities that can help your organization make an evaluation:
Orchestration
Orchestration is how the framework decides which agent or tool acts next, passes information between steps, and recovers when something fails.
Memory and State
Agents need to remember what they have done within a task and, sometimes, across tasks. Memory lets an agent maintain context over a long workflow, while persistent state lets it pause and resume.
Tool and Data Access
Agents become useful when they can call tools: a search index, a policy database, a ticketing system, an internal API.
Guardrails
Guardrails are the rules that keep an agent inside acceptable behavior, including input validation, output filtering, action approval steps, and hard limits on what the agent may do without a human.
Evaluation Criteria for Regulated Environments
Use the following matrix to compare any agentic AI framework against the standard a regulated program needs, the same way you would assess any new control through continuous control monitoring.
| Evaluation criterion | What to look for | Why it matters for a regulated program |
|---|---|---|
| Auditability and logging | Complete, tamper-evident logs of every agent decision, tool call, and output | Supervisors expect a reconstructable record of how a decision was reached |
| Human-in-the-loop controls | Configurable approval steps before consequential actions execute | Keeps accountability with a named person, not the software |
| Permissioning and least privilege | Per-agent scoping of tools, data, and actions, with clean revocation | Limits blast radius if an agent behaves unexpectedly |
| Observability and monitoring | Real-time visibility into agent behavior, errorstd>Lets the second line detect problems before they compound | |
| Model and data lineage | Traceability of which model version and data sources informed an action | Supports model risk management and reproducibility |
| Vendor transparency | Clear documentation of how the framework handles data, updates, and failures | Due diligence and third-party risk reviews depend on it |
Where Agentic AI Fits in Banking Risk and Compliance
Agentic AI in banking works best where the task is repetitive, rules-bound, and well-documented, yet still consumes skilled analyst time. A strong fit for these tools includes:
Regulatory change triage
An agent can monitor rule changes, identify the ones relevant to the institution, and draft a first-pass impact assessment for a human to confirm.
Control testing
An agent can gather evidence, check it against a control’s expected state, and flag exceptions.
Issue management
Agents route newly identified issues to owners and assembling the supporting documentation.
These deployments aim to spend scarce human judgment where it counts rather than on document retrieval. Claims that autonomous software can own a regulated decision outright deserve skepticism, both because the technology is immature and because accountability cannot be delegated to a tool.
Governing an Agentic AI Framework with the NIST AI RMF
The NIST AI Risk Management Framework, released in January 2023, gives financial institutions a voluntary but widely referenced structure built on four functions: Govern, Map, Measure, and Manage. Each applies cleanly onto an agentic deployment and onto broader AI risk management practice:
- Govern: Establishes the policies, roles, and accountability for AI use, including who may approve an agent for production.
- Map: Identifies where and how the agent operates and what could go wrong in that context.
- Measure: Defines how you will evaluate the agent’s performance, errors, and impact over time.
- Manage: Covers ongoing monitoring, incident response, and the decision to retrain, restrict, or retire an agent.
Governed risk and compliance platforms increasingly embed agentic capabilities. Ask Kaia, for example, applies agentic features within a controlled environment where actions are logged and routed for human review.
Frequently Asked Questions
What is an agentic AI framework in simple terms?
It is the software that lets you build, run, and supervise AI agents. The agent is the autonomous part that pursues a goal and takes actions; the framework provides the memory, tool connections, coordination, and oversight controls the agent needs to operate reliably.
How is agentic AI different from generative AI?
Generative AI responds to a single prompt and produces an output, such as a summary or draft. Agentic AI pursues a goal across multiple steps and can take actions in real systems, like updating a record or triggering a workflow.
How does the NIST AI RMF apply to agentic AI?
The NIST AI Risk Management Framework organizes AI oversight into four functions: Govern, Map, Measure, and Manage. For an agent, that means defining who approves it, documenting where it operates, measuring its performance and errors, and monitoring it in production with a path to restrict or retire it.
The agentic AI framework you choose will sit inside your control environment, so evaluate it the way you would evaluate any control. Start with repetitive, well-documented tasks, keep decision rights with named people, and align the deployment to a recognized structure such as the NIST AI RMF.
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