The AI world is moving fast, but one thing is becoming clear: generative AI is just the beginning. We’re entering the era of agentic AI — systems that don’t just respond, but act. Systems that reason, make decisions, and get things done in the real world. I had the opportunity to moderate a panel with some of the people pushing this frontier forward, and it wasn’t theoretical. This was about applied autonomy, with NVIDIA Jetson right in the middle of it.
And it’s not just a tech story — it’s a shift in architecture, in thinking, and in how we build systems that live at the edge.
What’s “Agentic” Really Mean?
At a high level, agentic AI refers to autonomous software (or hardware) agents that can understand goals, break them into tasks, and execute in real time — often in noisy, unpredictable environments. Unlike traditional inference or generative models, agentic AI is goal-driven and continuous, operating more like a human teammate than a passive tool.
The conversation anchored on this key distinction: Agentic AI doesn’t just respond — it takes initiative.
Enter Jetson: From Thought to Action at the Edge
If these agents are going to interact with the physical world, they can’t live entirely in the cloud. They need to run close to the sensors and actuators they work with — and they need compute that’s fast, efficient, and compact. That’s where NVIDIA Jetson comes in.
Jetson sits at the intersection of perception and control — perfect for enabling edge-native AI that sees, understands, and does. In the panel, we saw this play out in a real deployment involving smart retail and facility awareness.
The Use Case: Retail + Ops + Jetson
Cahlen Humphreys, one of the panelists and a seasoned developer in this space, walked us through a solution his team built to provide agentic awareness in a retail store. The use case: combining Jetson-enabled edge compute with camera feeds and store inventory systems to enable AI-driven kiosk agents that can interact with customers, monitor inventory conditions, and alert staff — autonomously.
Here’s how the architecture breaks down:
- Jetson-powered devices at each kiosk process video locally to identify context: who’s nearby, what they might be interested in, and whether staff action is needed.
- A local knowledge base (built from documents, schedules, and catalog data) gives the agent context about the space — so it doesn’t just recognize someone, it knows why they might be there.
- A multi-agent orchestration layer allows different kiosks and edge devices to collaborate — so one kiosk might greet a visitor while another adjusts signage based on real-time flow.
- And critically: no cloud dependency for core functionality. These are truly edge-native agents that can continue operating even when disconnected.
“What we’re building isn’t just smart hardware — it’s situationally aware AI that can act in real time,” Cahlen said during the panel. “Jetson gives us the compute we need to make these systems feel intelligent, not scripted.”
From Single Agents to Ecosystems
A key theme from the panel was the move from individual agents to networks of cooperating agents. Instead of one kiosk trying to do it all, you have multiple Jetson-enabled endpoints sharing observations and dividing tasks — almost like how different employees in a store or office might coordinate.
Imagine one kiosk detects a spike in foot traffic. It notifies a second one to switch into “fast info” mode, reducing interaction times. A third system flags inventory risk based on what’s being picked up most. None of this needs human intervention. And again, it’s happening on-device, in real time.
Final Thoughts: Why This Matters
The shift to agentic AI isn’t just a cool research trend. It’s a fundamental change in how we build systems — from reactive, cloud-tethered endpoints to proactive, real-world intelligence that operates at the edge.
Jetson makes this not only possible, but practical.
If you’re building solutions for smart cities, retail, healthcare, or logistics, and you’re not thinking about agentic AI + edge compute, you’re going to be playing catch-up. The panel wasn’t full of vaporware. It was teams deploying this today. And they’re seeing the value: responsiveness, resilience, privacy, and control.
We’re not waiting for the future. We’re already building it.
Interested in implementing agentic AI solutions for your organization? Contact our team to discuss how we can help you build intelligent, autonomous systems that operate at the edge.