This interview was filmed on location at The Helix, Booz Allen’s Center for Innovation in Washington, D.C., as part of the event DE25: Driving Outcomes through Data. The program features top technology leaders from the public and private sectors sharing insights on cloud transformation, agentic AI, fraud prevention, and data governance. Through a series of dynamic conversations, the program captures how agencies are aligning digital infrastructure with mission needs to deliver real results for the American people. Watch the full show.
Artificial intelligence in government is moving past experimentation and into execution—but what’s coming next goes far beyond large language models and chatbot interfaces. In the Driving Outcomes through Data segment titled “Harnessing Agentic AI for Critical Federal Missions,” Shane Shaneman, Senior AI Strategist for the U.S. Public Sector at NVIDIA, and Sahil Sanghvi, Vice President in the Chief Technology Office at Booz Allen, offer a compelling look into the next frontier: agentic AI.
This new generation of artificial intelligence shifts the paradigm from passive response systems to digital agents capable of autonomous reasoning, decision-making, and collaboration—all designed to scale mission impact across the federal landscape.
“It’s not just about asking questions anymore,” says Shaneman. “Agentic AI can break down complex tasks, execute on them independently, and even coordinate with other agents to get the job done.”
What Is Agentic AI?
At its core, agentic AI represents a step beyond traditional automation and generative AI tools. While most current models rely on human input for prompting and decision-making, agentic AI systems can reason, act, and adapt—essentially functioning as digital teammates embedded in mission workflows.
These agents are not simply hardcoded automation scripts. They are systems that learn tasks, access tools, gather context, and make real-time decisions based on mission-specific data. The technology enables multi-step orchestration of tasks without requiring constant human oversight.
“Think of these agents as digital assistants or even digital employees,” says Sanghvi. “They work alongside humans, understand their tasks, and take action—saving time, money, and ultimately, lives.”
From Linear Productivity to Exponential Impact
One of the key distinctions highlighted in the conversation is the difference in scale that agentic AI brings. Traditional digital transformation tends to result in linear improvements—faster processes, reduced errors, improved reporting. Agentic AI, however, introduces exponential productivity gains, particularly when multiple agents collaborate or when new agents are introduced on the fly.
Shaneman notes that recent advancements like the Model Context Protocol—an emerging interoperability standard among AI models and systems—are paving the way for federated, agent-to-agent communication that will enable entire ecosystems of AI to function in sync.
“We’re seeing a shift from one-shot prompts to persistent systems that can learn, reason, and collaborate across domains,” he says.
This evolution allows agencies to plug in models tailored to specific domains—finance, logistics, public safety—and rapidly integrate them into complex workflows, often without rewriting existing systems.
The Role of Data and Semantic Infrastructure
While the vision of agentic AI is powerful, both speakers agree that data access and structure are critical prerequisites. Sanghvi emphasizes the importance of building a semantic layer—a standardized, well-governed data foundation that provides agents with contextual understanding and access controls.
Without this infrastructure, agents may lack the clarity and security needed to execute reliably.
“If the agents don’t have access to well-structured, mission-relevant data, they’re flying blind,” Sanghvi warns. “The semantic layer gives them the context they need to act effectively—and safely.”
This is especially important in government environments where data classification, privacy, and security are paramount. Agentic systems must be able to access only what is appropriate, with logging, oversight, and fail-safes built in from the start.
Balancing Innovation with Governance
As federal agencies embrace emerging AI capabilities, Shaneman and Sanghvi urge them to balance short-term experimentation with long-term planning. It’s easy to get caught up in the excitement of pilot projects, but real value comes from building repeatable, scalable systems that can adapt and evolve with changing mission demands.
Sanghvi frames the progression in three stages:
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Short-term value — Prove impact quickly with measurable outcomes.
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Mid-term infrastructure — Build scalable, secure, and compliant systems.
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Long-term transformation — Rethink mission delivery entirely with AI-native architectures.
“We’re not just trying to automate old processes,” says Sanghvi. “We’re reimagining how the mission gets delivered in the first place.”
This shift requires collaboration between technical teams, mission stakeholders, and policy leaders—all working together to align AI capabilities with real-world priorities.
Mission-Driven Use Cases Leading the Way
Both speakers point to real-world applications already in motion. Shaneman references agentic systems used for federal service delivery, decision support, and intelligence analysis, where speed and precision are essential. Sanghvi notes use cases in cyber defense, supply chain optimization, and case management, where the need for autonomous, trusted systems is growing rapidly.
The potential is clear: reduce cognitive burden, enable faster decisions, and allow federal employees to focus on the human elements of the mission—not the mechanical ones.
Final Thoughts: Building for What’s Next
As AI adoption accelerates across the federal government, Shaneman and Sanghvi emphasize that agentic AI isn’t science fiction—it’s already here. But it requires new thinking around architecture, governance, and culture.
Agencies that invest in semantic infrastructure, responsible AI practices, and workforce readiness will be best positioned to unlock the full value of this transformation.
“The agencies that come to us and say, ‘Here’s the problem we need AI to solve’—those are the ones that are ready,” says Sanghvi. “It’s about purpose-built innovation, not just using AI for AI’s sake.”
In the age of agentic AI, the future of mission delivery won’t just be faster. It will be fundamentally different—more adaptive, more intelligent, and more impactful than ever before.