The Agent Harness: Building Secure Sandboxes for Autonomous AI Workloads
About this episode
Ivan Burazin discusses the infrastructure needs for AI agents and the limitations of traditional cloud providers.
If AI agents are the new digital knowledge workers, where exactly do they do their work? In this episode of the MAD Podcast, Ivan Burazin, CEO of Daytona, joins us to unpack the emerging infrastructure stack for AI agents and explain why every agent needs its own secure, stateful "computer." We explore the technical realities of sandboxes, dive into why legacy, stateless hyperscalers weren't built for these new workloads, and break down the mechanics of microVMs and custom schedulers alongside a contrarian prediction on an impending CPU shortage. Finally, Ivan delivers an absolute masterclass on product-led growth, community building, and go-to-market strategy for technical founders. (00:40) Intro (02:13) What is an AI agent sandbox? (03:17) Security risks of running agents locally (05:17) Stateful vs. stateless hyperscalers (07:04) The history of cloud IDEs and the end of localhost (09:45) Do all AI agents need a sandbox? (12:26) Sandbox use cases: RL evals & background agents (14:10) Unpacking the emerging AI Agent Stack (16:20) The unsolved problem of agent memory and learning (19:37) Where sandboxes fit in the agent harness (21:35) OpenAI, Anthropic, and agent…
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