What Is Agentic AI?

How Does Agentic AI Work?

Agentic AI works through a continuous cycle of gathering information, reasoning, planning, acting and learning. It starts by gaining clarity on the task at hand, pulling in relevant data from different sources based on its permissions to understand the context.

“After evaluating the context, it identifies patterns and formulates a plan of action — anticipating roadblocks and adjusting as needed,” says Dorit Zilbershot, group vice president of AI innovation at ServiceNow.

Agentic AI then leverages the tools and systems it can access to execute against that plan, adhering to defined policies and safeguards to ensure alignment with business goals and organizational governance frameworks.

“Then it assesses outcomes, continually optimizes its plan, and incorporates feedback to get smarter and more effective over time,” Zilbershot says.

What Makes Agentic AI Different from Traditional AI?

Agentic AI represents a clear departure from traditional AI by moving from passive, one-off responses to proactive, adaptive engagement.

Traditional AI was “very reactive; you prompted a response,” says Amanda Saunders, director of enterprise generative AI software at NVIDIA. Earlier models performed basic perception tasks, such as identifying anomalies in an image or generating content based on a single prompt, but they lacked memory and context awareness.

In contrast, agentic AI introduces what Saunders calls “a whole new world of productivity” by enabling systems to think, reason and iterate.

“Reasoning is simply just thinking through the problem, evaluating the response that you come up with and making sure that whatever you’re delivering is highly accurate,” she says.

This shift allows agentic AI to deliver intelligent assistants capable of working alongside humans, adjusting to feedback and making autonomous decisions that align with user goals.

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Real-Time Compliance and Risk Mitigation Through Agentic Systems

Agentic AI can play a vital role in helping agencies manage real-time compliance and risk by continuously monitoring large volumes of data for anomalies and thresholds.

“There’s a lot of data that must be sorted in real time, and AI can be always-on, reviewing and analyzing to ensure that these contracts are in compliance,” Saunders says. “That agent is always running in the background, providing helpful information to those human employees.”

Agentic AI can empower organizations to stay ahead of evolving threats by proactively monitoring activity and intervening when needed.

“It can pause actions, escalate issues and surface risks to the right stakeholders before they become widespread,” Zilbershot says.

Because agentic AI learns from every interaction, it also becomes increasingly effective at enforcing compliance and managing risks in real time.

“This helps agencies operate with confidence while staying aligned to evolving federal standards,” Zilbershot explains.

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Mapping User, Data and Access Relationships Dynamically

Agentic AI systems can build and refine understanding of data relationships by learning from user interactions and maintaining memory over time.

“As it gets access to more information, and as it learns and maintains that memory from the actions that it takes, it’s able to understand where data exists and update different relationships based on those human interactions,” Saunders says.

By incorporating feedback loops and analyzing how users reframe queries, agentic AI can continuously adapt its understanding of intent and improve the accuracy of future responses.

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How Agencies Can Operationalize Agentic AI Today

Agencies can start using agentic AI in areas like process automation, compliance monitoring and data analysis.

“By automating routine tasks, agencies can free up staff to focus on mission-critical priorities,” Zilbershot says.

She recommends starting with targeted pilot programs enabling agencies to test how agentic AI performs in federal environments before expanding across departments. Agencies should begin running AI agents with strong supervision, requiring human approval before any changes or actions are taken, and gradually expand use as confidence and governance structures mature.

“With the right guardrails, agencies can move swiftly from experimentation to enterprisewide AI transformation,” Zilbershot says.