MLOps Community Podcast

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 needed. New spend: $80k. It's the kind of move that only makes sense once you realize more agentic autonomy isn't always the win it's sold as. From there we get into the machinery Wayfair has built to make cost ownership feel less like a punishment and more like a superpower: guardrails baked straight into coding copilots, predictive alerts that catch a team blowing its budget before the month even ends, a "spend captain" system that pushes budget decisions down to the people closest to the work, and a genuinely strange new question every tech leader is starting to ask: if you were staffing a company with a team of AI agents instead of people, what would that org chart even look like?

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 dig into DSPy: signatures, the GEPA optimizer, and the brand-new Flex optimizer, which rewrites your code instead of just your prompt, complete with a real before-and-after on cost and accuracy. We also get into why so many AI-built apps and websites end up looking identical, the actual difference between an agent and a workflow, what Drew learned a year after shipping a code library with no code in it, and why he thinks the most valuable thing you can do right now is close the laptop and go talk to people.

OpenAI's Codex developer experience lead sits down with a former comedian turned ML engineering lead for a conversation about what happens when computer use agents stop being a novelty and start actually running your day. The conversation moves through building an AI-powered morning brief that reads every email, Slack message, and tweet before you've even opened your laptop, letting pinned threads check in on themselves every 30 minutes, and a skills system built to mirror how a person actually writes and reviews code. There's a close look at the guardrails and permission layers that keep an autonomous agent from pushing to the wrong repo or replying to the wrong tweet, how a codebase merging thousands of pull requests a day survives thanks to self-healing review before anything hits CI, and the idea of AI deference - when an agent should push through a task alone versus stop and ask for help. The back half gets personal: why developing taste and vocabulary now matters more than working harder, what it actually takes to delegate to an agent the way you'd onboard a new employee, and why this might be the year voice-orchestrated computer use finally makes everyone feel like they're talking to Jarvis.

In this episode, we're joined by Josh Collier, FinOps Lead at Superhuman (formerly Grammarly), to explore what it really costs to run AI at scale and why the rules of the game changed faster than anyone expected.

In this episode, we're joined by David Soria Parra, Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP), to explore the biggest evolution of the protocol since its launch and why MCP is becoming the foundation for the next generation of AI agents.

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.

Jeremiah Lowin — founder and CEO of Prefect and creator of FastMCP — traces how a two-hour struggle to build a basic MCP server turned into FastMCP, a side project that Anthropic folded into the official SDK and that now underpins roughly 70% of all MCP servers. He argues the biggest real-world use of MCP isn't reaching external customers but serving data and business logic to teams inside a company (especially data, platform, and ML teams building self-serve data platforms), where the client is known and trusted. He pushes back on the "MCP is dead / it should just be a CLI" narrative, blaming bad clients that dump every tool into an LLM's context rather than the protocol itself. The centerpiece is Prefab, a newly launched Python DSL of 100+ prefabricated, fully interactive React components (ShadCN-based) that let you return a chart, table, or form straight from a tool as an MCP App — token-efficient, streamable, and routed to the user's UI without polluting the LLM's context window. He closes with candid stories about his OpenClaw agent deleting his conference slides (forcing him to rebuild with Claude Code), how he uses a memory-driven home agent for family logistics, and the intimate, builders-only PyAI conference Prefect co-hosts with Pydantic.

Stephen O'Grady has spent 20+ years at RedMonk tracking what developers actually adopt — not what the hype cycle says they should. In this conversation from the MCP Dev Summit, he breaks down why MCP became the fastest de facto standard he's ever measured, why "SaaS is dead" is laughable, and what really happens to your infrastructure when one developer turns into a swarm of twenty.

Matt DeBergalis, CTO and co-founder of Apollo GraphQL, makes the case that AI agents should be treated as untrusted — maybe even adversarial — code running inside your firewall. In this conversation with Alex Salkever, Matt breaks down why the rush to wire agents into every enterprise system through MCP is creating a brand-new security surface, and how GraphQL's typed, governed "supergraph" model gives teams a safer way to connect agents to their APIs. It's a sharp, practical look at the collision of MCP, GraphQL, and enterprise AI from one of the people building the plumbing.

Shaun Smith is a software engineer, open source advocate, and MCP server maintainer at Hugging Face — creator of Fast Agent, the first agent framework built from the ground up around the Model Context Protocol. In this conversation from the MCP Dev Summit in New York, Shaun and Demetrios dig into why powerful models are "liquifying" open source libraries, how MCP Apps are reshaping AI interfaces, and what it means when the distribution of ideas matters more than the distribution of code.

Ben Morss is a Developer Advocate at DeepL — the German AI translation company — and over roughly ten days, he took Model Context Protocol on the road: 10 cities, 10 talks, 4 countries (US, Canada, France, Germany). Along the way, he went from "I didn't know what MCP was at all" to teaching packed rooms how to build a server from scratch. In this conversation with host Alex Saltkever at the MCP Dev Summit North America, he shares the one lesson that kept surprising everyone.

Cornelia Davis has spent three decades watching programming abstractions climb — from assembly to C to Java to the cloud. Now at Temporal (and author of the influential book Cloud Native Patterns), she argues that natural language is the most radical abstraction jump yet: the first one that isn't deterministic. Recorded at the MCP Dev Summit in New York, this conversation covers how durable execution becomes the safety net underneath probabilistic AI systems.

Denny Lee is PM Director, Startups & Ecosystem at Databricks, a longtime Apache Spark, MLflow, and Delta Lake contributor — and one of the people behind Omnigent, the open-source meta-harness Databricks just released under Apache 2.0. He joins Demetrios to explain why the industry is moving from models to harnesses to meta-harnesses, why token spend is replaying the CapEx-to-OpEx shift all over again, and why he's using debating AI agents to plan a matcha farm in Taiwan.

Kingsley Madikaegbu is the founder of HealID, a startup building agentic AI on top of the Model Context Protocol (MCP) for one of the most heavily regulated environments there is: healthcare. Recorded at MCP Dev Summit North America in New York, Kingsley sits down with Alex Salkever of the Agentic AI Foundation to break down how you give patients, doctors, caregivers, and family members each their own agent over the same medical record — without breaching HIPAA, leaking PHI, or letting an agent quietly go off the rails.

Jay Hack is the Head of AI at ClickUp and the founder of Codegen, the autonomous coding-agent startup ClickUp acquired in late 2025. He built one of the first ticket-to-pull-request background agents in enterprise software — before Claude Code existed — and has been working in AI since the SIFT-and-SVM days of 2008. In this freewheeling conversation with Demetrios, he makes the case that coding agents and general knowledge-work agents are converging fast, and that the real battle ahead is over context, not capability.

Sam Partee (CTO & co-founder of Arcade.dev) and Nate Barbettini (Founding Engineer at Arcade.dev) sit down at the MCP Dev Summit to unpack what nobody wants to admit about the Model Context Protocol: the security model is still full of sharp edges. From tool poisoning and prompt injection to why OAuth got bolted onto the spec, this is a builder 's-eye view of where MCP breaks — and how to ship agents safely anyway.

Shahram Anver is the Co-Founder and CEO of Cleric, the autonomous AI SRE that investigates and root-causes production issues like an experienced teammate — often in under two minutes. Before Cleric, Shahram led MLOps, DevOps, and FinOps platform engineering at Gojek, Southeast Asia's super-app. In this conversation, he breaks down why production operations never kept pace with AI-accelerated development, and why the real unlock for an AI SRE isn't faster triage — it's an agent that *learns* and compounds operational memory across your whole org.

Naseem Al-Naji is the co-founder of MCPcat.io and the creator of Opal — a builder with deep roots in privacy-first developer tooling. In this conversation, he breaks down why MCP servers have become a black box in production, and how MCPcat gives teams X-ray vision into how agents and users actually behave.

Stanislas Polu is Co-Founder & CTO of Dust — the enterprise AI agent platform used by 51,000 workers at 3,000+ companies. Before Dust, he spent three years on OpenAI's research team under Ilya Sutskever, working on mathematical reasoning in language models, and prior to that was an engineer at Stripe. He brings a rare combination of frontier AI research and product-building experience to the enterprise agent space.

James Everingham is the CEO and Co-founder of Guild.ai — the AI agent control plane for production teams. With roots at Netscape, Instagram (Head of Engineering), and Meta (Head of Dev Infra, leading a 1,000-person org), James brings rare, hard-won expertise to the challenge of operating AI agents at scale.

Thiago Cardoso is the Director of Data & AI at iFood and the architect behind iFood Pago's AI agent platform. This fintech system serves millions of restaurants across Brazil through WhatsApp and the iFood app. In this episode, he breaks down what it actually takes to ship agentic AI in production at scale.

Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company — now he's back, betting that logs are all you need to replace Datadog.

Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE — the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.

Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation) from Just Eat Takeaway.com join the MLOps.community to pull back the curtain on how one of Europe's largest food delivery platforms is running an internal innovation engine. From autonomous delivery robots to agentic AI voice assistants, they share what it actually takes to build like a startup inside a 40,000-person company.

Pramod Krishnan is a Managing Director - AI Managed Services at PwC, specializing in enterprise AI transformation — helping large organizations move from AI experimentation to production operating models. In this episode with Demetrios, Pramod breaks down exactly what the OpenClaw wave means for enterprises, and the control frameworks PwC uses before a single agent touches production.

Rafael (Head of Innovation, iFood) and Daniel (Data and AI Manager, iFood) pull back the curtain on ILO-Agent — iFood's conversational AI ordering system built for 200 million users across Latin America. Recorded live at AI House Amsterdam, this conversation goes deep on the engineering and product decisions behind building recommendation systems, agentic AI, and why the speed of your AI's response might actually be destroying user trust.

Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.

Anurag Beniwal (Member of Technical Staff at ElevenLabs) breaks down the real-world challenges of building voice agents—from latency, transcription accuracy, and turn-taking to the tradeoffs between cascaded systems and end-to-end speech models. The conversation explores why production systems rely on “constellations” of models, how to design for non-technical users (especially in customer support), and why voice unlocks richer context—but introduces far more complexity than chat. Ultimately, it’s a deep dive into making voice AI practical, reliable, and usable at scale.

Jesse Vincent breaks down how modern “agentic” software development is shifting from writing code to managing intelligent systems. He shares how his Superpowers toolkit uses structured workflows, skills, and subagents to turn vague ideas into executable plans—emphasizing that clarity of intent matters more than coding itself. The conversation explores how AI agents can be guided using psychology, why separating roles (planner, implementer, reviewer) leads to better outcomes, and how iteration—not perfection—builds powerful workflows. Ultimately, the future of software isn’t code—it’s specs, judgment, and orchestrating agents to do the work.

Most teams treat evals like a last-minute checkbox—ship first, panic later—but that’s exactly backwards. The real edge comes from treating evals as a continuous, evolving system from day one, not a static test suite. Because here’s the uncomfortable truth: LLMs don’t fail cleanly or consistently, and neither do your users. If you’re not constantly adapting how you evaluate, you’re basically flying blind—just with more features to hide it.