Why context will define agentic AI at the edge
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In this episode of The Data Movement, host Paul Langston sits down with Iman Anvari, director of advanced technology at Seagate, to explore what’s really coming next for AI — and what it will take to make autonomous systems work outside the lab.
Drawing on insights from NVIDIA’s GTC and Seagate’s hands‑on innovation work, Iman shares why the industry has reached a moment of real change. AI models are getting smarter — but capability alone isn’t enough. The challenge, he points out, is context: understanding what’s happening, where it’s happening and how to respond.
That’s where the edge comes in.
The conversation explores how robotics brings together hardware, software, and data infrastructure in a way few workloads can — and why latency is the hard constraint that moves compute out of centralized clouds and closer to where data is generated. When decisions have to happen in real time, relying on cloud round trips just doesn’t scale.
Paul and Iman unpack why autonomous systems need more than raw compute. They rely on layered memory and storage working together to deliver real‑time awareness and continuous learning.
In this episode, you’ll hear about:
- Why robotics brings compute, storage and real‑world data together
- How edge “micro data centers” make AI automation practical
- Why latency is the key driver pushing intelligence to the edge
- How context becomes the constraint for autonomous agents
- The tiered memory architectures required to support agentic AI at scale
Iman delivers a clear, systems‑level perspective on what it takes to move agentic AI from concept to reality. For teams building at the edge, it’s a reminder that progress isn’t just about more capable models — it’s about putting intelligence closer to the action, with the right data infrastructure to support it.
