Agentic AI

A category of AI systems capable of independently making decisions, interacting with their environment, and optimizing processes without direct human intervention.

Source: AIEOG AI Lexicon (Feb 2026), adapted from doi.org/10.1016/j.array.2025.100399 and doi.org/10.3390/fi17090404

Agentic AI describes systems that can act on their own. Unlike traditional AI tools that respond to a single prompt and return an answer, agentic AI systems can perceive their environment, decide what to do next, take action, and adjust their approach based on the results. They operate with a degree of autonomy that goes beyond simple automation.

In practical terms, an agentic AI system might receive a high-level objective (“review these 500 alerts and escalate the ones that meet these criteria”) and then work through the task independently, making intermediate decisions along the way without a human approving each step.

This is a meaningful distinction from earlier AI paradigms. A standard machine learning model scores a transaction and returns a risk rating. An agentic system could score the transaction, pull additional data from external sources, compare the result to historical patterns, draft an initial investigation summary, and route the case to the appropriate analyst. The system is making multiple chained decisions, not just one.

Agentic AI is entering financial services at a moment when institutions face pressure to do more with fewer resources. Compliance teams are understaffed, alert volumes are rising, and regulatory expectations continue to expand. Agentic AI promises to help by taking over multi-step workflows that currently require significant human effort.

However, the same autonomy that makes agentic AI useful also creates governance challenges that financial institutions must address:

• Accountability gaps. When an agentic system makes a chain of decisions autonomously, it becomes harder to attribute a specific outcome to a specific decision point. Regulators expect clear accountability, and institutions need to demonstrate who (or what) made each decision and why.
• Scope creep. Agentic systems can interact with their environment in ways that go beyond their original design. Without proper guardrails, an AI agent could take actions outside its intended scope.
• Supervision requirements. Regulatory frameworks like SR 11-7 require human oversight of model outputs. Agentic AI systems that operate autonomously challenge the traditional human-in-the-loop model and require institutions to rethink how oversight is structured.
• Audit trail complexity. Examiners expect institutions to produce clear records of how decisions were made. The multi-step, branching nature of agentic workflows makes documentation and reproducibility more complex than single-model systems.

The AIEOG’s inclusion of agentic AI as a defined term reflects the financial sector’s recognition that this technology is already being adopted and needs governance standards.

1. Define autonomy boundaries. For every agentic AI deployment, document what the system is authorized to do independently and where human approval is required. These boundaries should be enforced technically, not just documented in policy.
2. Build approval gates into agent workflows. High-risk decisions (filing a SAR, denying an application, escalating to a regulator) should require human sign-off even if the agentic system prepares the work.
3. Log every agent action. Agentic systems should produce detailed, timestamped logs of every decision, data retrieval, and action taken. These logs are essential for audit, examination, and incident investigation.
4. Include agentic systems in your AI use case inventory. Each agentic AI deployment should be documented with its objective, scope of autonomy, data access, decision authority, and human oversight model.
5. Test for unintended behavior. Agentic systems can behave in unexpected ways when encountering novel situations. Validation processes should include scenario testing that deliberately puts the agent in edge cases.
6. Establish kill switches. Every agentic deployment should have a clear, tested process for immediately halting the system if it begins operating outside its intended parameters.

AI Agent, Human-in-the-Loop (HITL), AI governance, Guardrails, AI system, Responsible AI, Override