Subvolts take AI agents need new infrastructure; learn how isolated computers unlock agent capabilities. Jan 11, 2026 - Developers
- Learn how to architect and deploy AI agents using specialized "devbox" infrastructure.
- Business
- Understand the infrastructure needs for integrating AI agents into enterprise workflows.
This episode offers a solid interview with an expert discussing a critical, emerging aspect of AI deployment.
#312 Jonathan Wall: AI Agents Are Reshaping the Future of Compute Infrastructure
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Craig Smith interviews Jonathan Wall on Eye on AI about the need for new compute infrastructure for AI agents. Wall explains agents differ from servers, requiring isolated "devboxes." The discussion covers scaling agents, building trust via benchmarking and human-in-the-loop systems, and the future of enterprise work with agent-driven systems.
Imagine AI agents as smart helpers needing their own special workstations, not just shared office space. This episode explains why these "devboxes" are crucial for making AI helpers work reliably and at scale for businesses.
This episode explains why current cloud infrastructure struggles with AI agents, a key challenge for scaling AI. It's for developers and business leaders exploring agent deployment. This connects to frontier labs' work on agent capabilities and efficient deployment, as they build more sophisticated AI systems that need robust, specialized compute.
- HostCraig S. Smith
- GuestJonathan Wallfounder and CEO, Runloop AI
- Guest: Jonathan Wall, founder and CEO of Runloop AI
- Show: Eye on AI
- Episode Number: #312
- Length: 52 minutes
- Core Concept: AI agents require a new approach to compute infrastructure.
- Key Technology: Isolated computers, or "devboxes," for AI agents.
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
- compute infrastructure
- Jonathan Wall
- the underlying hardware and software systems that run AI applications.
- devboxes
- Jonathan Wall
- isolated computers specifically set up for AI agents to run in.
- human-in-the-loop
- Jonathan Wall
- a system where humans are involved in the AI's decision-making process.
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45 minQuestions people ask
- Why do AI agents need different infrastructure than traditional servers?
- AI agents behave very differently from traditional servers, requiring their own isolated computers or "devboxes" to unlock new capabilities.
- How can trust and accuracy be built for AI agents?
- Trust and accuracy can be built through benchmarking and incorporating human-in-the-loop workflows.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.