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Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)
- Indexed episodes, last 90 days
- 16
- Latest publication
- Sep 18, 2026
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- Jul 3, 2026
Latest episodes
Walking Tokyo Talking Agent Protocols (opens the original)
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Two people, a wrong turn into a back alley, a community garden, and about thirty minutes of arguing about protocols on the streets of Tokyo. The guest is Angie Jones , VP of Developer Experience at the Agentic AI Foundation , fresh off launching AGNTCon + MCPCon in China before the Tokyo stop. She opens with what she learned there: a mobile-first, super-app world where the integration problem most of us obsess over barely exists, where every conversation about agents is really a conversation abo
Why Cost Per Million Tokens Is A Useless KPI? (opens the original)
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A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story. Recorded at FinOps X in San Diego, this conversation brings together Abhinav Lad , who leads cloud and AI finance at Palo Alto Networks, and Kuntal Patel , who runs the cloud engineering function behind it. They explain what happened when agents entered the picture, and AI stopped behaving like a service anyone could forecast. The shor
The Five-Layer Cake Approach to Scaling AI Without Wasting Money (opens the original)
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In this episode of Agentic Conversations, we sit down with Ambud Sharma , Principal Engineer at Pinterest , responsible for general technology efficiency, fresh off delivering a controversial keynote on AI infrastructure optimization at scale. Ambud walks us through his Five Layer Cake framework - a structured approach to driving efficiency across every level of the AI stack, from silicon and hardware procurement to model selection, inference engine design, and governance. We explore how decisio
The Winchester Mystery House Problem in AI Development (opens the original)
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AI models are starting to act like appliances, locked into one narrow way of working, instead of the flexible infrastructure they used to be. Drew Breunig , an AI and data strategist working with the Overture Maps Foundation, joins us to explain why, and what it means for anyone building something that doesn't look like Claude Code. Drew walks through his "Winchester Mystery House" idea: what happens once code gets so cheap to write that the only real bottleneck left is feedback. From there we d
How Predictive Analytics Stops Budget Overruns Before They Happen? (opens the original)
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Every engineer at Wayfair can now see, in real time, exactly what their code costs, and that's on purpose. Brent Eubanks , FinOps Architect at Wayfair , walks us through what happens when you stop treating AI spend as a finance problem and start treating it as an engineering one.The story that sticks with you: a team was burning $400k a month on an LLM-driven workflow, until they flipped the whole thing on its head - hard-coded logic doing the heavy lifting, AI called in only when it's actually
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