In this episode of Eye on AI, Craig Smith speaks with Jonathan Wall, founder and CEO of Runloop AI, about why AI agents require an entirely new approach to compute infrastructure.

Jonathan explains why agents behave very differently from traditional servers, why giving agents their own isolated computers unlocks new capabilities, and how agent-native infrastructure is emerging as a critical layer of the AI stack. The conversation also covers scaling agents in production, building trust through benchmarking and human-in-the-loop workflows, and what agent-driven systems mean for the future of enterprise work.

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(00:00) Why AI Agents Require a New Infrastructure Paradigm

(01:38) Jonathan Wall's Journey: From Google Infrastructure to AI Agents

(04:54) Why Agents Break Traditional Cloud and Server Models

(07:36) Giving AI Agents Their Own Computers (Devboxes Explained)

(12:39) How Agent Infrastructure Fits into the AI Stack

(14:16) What It Takes to Run Thousands of AI Agents at Scale

(17:45) Solving the Trust and Accuracy Problem with Benchmarks

(22:28) Human-in-the-Loop vs Autonomous Agents in the Enterprise

(27:24) A Practical Walkthrough: How an AI Agent Runs on Runloop

(30:28) How Agents Change the Shape of Compute

(34:02) Fine-Tuning, Reinforcement Learning, and Faster Iteration

(38:08) Who This Infrastructure Is Built For: Startups to Enterprises

(41:17) AI Agents as Coworkers and the Future of Work

(46:37) The Road Ahead for Enterprise-Grade Agent Systems

In this episode of Eye on AI, Craig Smith sits down with Anurag Dhingra, Senior Vice President and General Manager at Cisco, to explore where AI is actually creating value inside the enterprise.

Rather than focusing on flashy demos or speculative futures, this conversation goes deep into the invisible layer powering modern AI: infrastructure.
Anurag breaks down how AI is being embedded into enterprise networking, security, observability, and collaboration systems to solve real operational problems at scale.

From self-healing networks and agentic AI to edge computing, robotics, and domain-specific models, this episode reveals why the next phase of AI innovation is less about chatbots and more about resilient systems that quietly make everything work better.

This episodeis perfect for enterprise leaders, AI practitioners, infrastructure teams, and anyone trying to understand how AI moves from theory into production.

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(00:00) Why AI Only Matters If the Infrastructure Works
(01:22) Cisco's Evolution
(04:39) Connecting Networks, People, and Experiences at Scale
(09:31) How AI Is Transforming Enterprise Networking
(12:00) Edge AI, Robotics, and Real-World Reliability
(14:18) Security Challenges in an Agent-Driven Enterprise
(15:28) What Agentic AI Really Means (Beyond Automation)
(20:51) The Rise of Hybrid AI: Cloud Models vs Edge Models
(24:30) Why Small, Purpose-Built Models Are So Powerful
(29:19) Open Ecosystems and Agent-to-Agent Collaboration
(33:32) How Enterprises Actually Adopt AI in Practice
(35:58) Building AI-Ready Infrastructure for the Long Term
(40:14) AI in Customer Experience and Contact Centers
(44:14) The Real Opportunity of AI and What Comes Next

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.

Visit https://agntcy.org/ and add your support.


Most large language models today generate text one token at a time. That design choice creates a hard limit on speed, cost, and scalability.

In this episode of Eye on AI, Stefano Ermon breaks down diffusion language models and why a parallel, inference-first approach could define the next generation of LLMs. We explore how diffusion models differ from autoregressive systems, why inference efficiency matters more than training scale, and what this shift means for real-time AI applications like code generation, agents, and voice systems.

This conversation goes deep into AI architecture, model controllability, latency, cost trade-offs, and the future of generative intelligence as AI moves from demos to production-scale systems.


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(00:00) Autoregressive vs Diffusion LLMs
(02:12) Why Build Diffusion LLMs
(05:51) Context Window Limits
(08:39) How Diffusion Works
(11:58) Global vs Token Prediction
(17:19) Model Control and Safety
(19:48) Training and RLHF
(22:35) Evaluating Diffusion Models
(24:18) Diffusion LLM Competition
(30:09) Why Start With Code
(32:04) Enterprise Fine-Tuning
(33:16) Speed vs Accuracy Tradeoffs
(35:34) Diffusion vs Autoregressive Future
(38:18) Coding Workflows in Practice
(43:07) Voice and Real-Time Agents
(44:59) Reasoning Diffusion Models
(46:39) Multimodal AI Direction
(50:10) Handling Hallucinations