Agentic Conversations (MLOps Community)

What happens when you give 200 engineers who have barely touched AI a coding assistant and a six-month deadline to rebuild their platform? Pepijn Senders, a principal engineer who previously led AI enablement at HelloFresh, has done exactly that, and he chose Sonnet over Opus on purpose. His reasoning is what he calls "Sonnet maxxing." A model that reasons less and stops sooner keeps developers involved, and that matters a lot when the codebase is banking software full of hard business rules. The result was 800,000 lines of code, about 60% of them written by Sonnet, shipped by a team that neve

An AI slips out of its sandbox. A chatbot won’t explain your cat’s stripes. And your next shopping spree might not involve you clicking anything. This episode asks whether “dangerously powerful” AI is a security concern, a marketing pitch, or a little of both. Then we put agents to work: booking trips, tackling German paperwork, drawing diagrams, and navigating the chaos of online shopping. Plus: what happens to checkout, advertising, and customer data when your best customer is a bot? Chapters:[00:00] Is AI Danger the Ultimate Sales Pitch?[00:43] Powerful Models, Payments, and Big Questions[0

What happens to the web when the ones browsing it are no longer people? Sarah Drasner, Area Tech Lead at Google, sits down with us ahead of her Agentic Web keynote at AGNTCon + MCPCon North America. She works through that question from both directions: rebuild the web so agents can use it properly, or build agents smart enough to handle the web as it already exists. We get into the split between computer-use and browser-use agents on one side and API and MCP agents on the other. We also watch a real example of the idea in action: an agent pulling speaker photos from Flickr, hunting down compan

Everyone is racing to build one agent that can do everything. Justin Schroeder, co-founder of Standard Agents, thinks the future looks more like thousands of tiny ones. Ahead of his talk on domain-specific agents at AGNTCon + MCPCon North America, he explains why tightly scoped agents might be the smarter bet, even if it feels like a step back toward the single-purpose machine learning models of a few years ago. We dig into standards as metadata you pin to an agent, so that it shows up with everything it needs in its harness. Then we test the idea against the obvious objection: if a big model

What happens to an MMORPG when half the players never log off? Issac Lee, who runs AI and blockchain experiments at NEXUS, has been testing that idea. His team is filling game worlds with agent players instead of scripted NPCs, so a new title feels alive from day one. We get into why agent-generated content could be the next step after studio-made and user-generated games. We also cover how agent populations might solve the chicken-and-egg problem every multiplayer launch faces, and what it means when your fellow players are relentless and never need sleep. Isaac walks through the NEXUS lab: a

Picture a startup with ten engineers hammering away on AI and one person lying awake over a $20,000 bill that hasn't arrived yet. That's the scenario we put to Jason Ward, who handles FinOps for AI at C.H. Robinson and recently joined the FinOps Foundation's AI working group. Jason's first answer is not glamorous: tag every AI resource so you know who owns it. The rest of the episode is what that makes possible. He walks through how C.H. Robinson runs AI across order entry, quoting, booking, and tracking, and why the team routes Anthropic models through Vertex AI to keep data locked down. Then

Caveman prompting has one rule: why use many words when few do the trick? It saves tokens on the way in and on the way out. Push it too far, though, and the output falls apart. So how far is too far? Nobody has benchmarked it yet, and that question opens our conversation with James Barney, Head of Forward Labs at MetLife. James spends his days connecting new AI capabilities to old business problems across dozens of regulatory regimes, and he still finds time to push code. He explains how the FinOps Foundation's AI working group took on the most basic question: which model for which workload, a

What happens after you’ve built your first MCP server and actually have to make it work in the real world? In this episode, James Ward dives into the more advanced side of MCP: observability, evals, tool design, code mode, authentication, and the challenges that appear once agents start using your server at scale. We also get into how AWS thinks about MCP across roughly 16,000 APIs, why inefficient tool design often gets blamed on MCP itself, and whether the future could involve more constrained, human-reviewable alternatives to full code mode. Along the way, James shares a great example of an

Tool descriptions tell an agent what a tool does. They don't tell it how to use five tools together, in the right order, following your conventions. That gap is where this conversation lives. Filmed at AGNTCon + MCPCon in Tokyo with Ola Hungerford, Principal Engineer for AI Enablement at Nordstrom and a maintainer of the Model Context Protocol, who spent the last several months turning a pattern everyone was quietly reinventing into an actual MCP extension. Ola walks through what skills over MCP really means: the server stops being a pile of tools and becomes a distribution channel, handing th

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 about the model, and where companies are now reaching for MCP and A2A precisely because they want to oper

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 short version: consumption went from linear to exponential almost overnight. Agents are goal-oriented rath

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 decisions compound across layers to unlock real business growth, and how the wrong choices can lock you into

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 c

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 aut

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 a

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. We discuss how AI token costs dropped 80% in two years, why that trend has sharply reversed with frontier models doubling in price, and how Josh rebuilt a single LLM workflow that cost $400k a month down to $80k by rethinking the architecture. He also shares how a cost calculator built in 15 minutes transformed the way his team estimates spend before running experiments, and why resea

David Soria Parra is an Engineering Lead at Anthropic and one of the core maintainers of the Model Context Protocol (MCP). We explore the biggest evolution of the protocol since its launch, and why MCP is becoming the foundation for the next generation of AI agents. We discuss why MCP is moving toward stateless communication, what developers misunderstand about state, sessions, and transport layers, and how lessons from real-world deployments at massive scale have shaped the protocol's future. We also dive into MCP v2, SDK migrations, protocol design, extension architecture, governance, develo

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. Huge thanks to PwC for supporting this episode! 💰 The 30% benchmark — What "good" actually looks like: real efficiency gains clients are reporting across engineering, finance, HR, and supply chain 🔄 Workflows, not use cases — Why isolated pilots and POCs never show up in EBIT

In this episode, we're joined by Jeremiah Lowin, Founder & CEO at Prefect and the creator of FastMCP, to explore how one of the most influential projects in the MCP ecosystem came to be - and where the protocol is heading next. We discuss the accidental origin of FastMCP, why Anthropic adopted it into the official SDK, what developers are getting wrong about MCP, and why Chris believes the biggest opportunity for AI agents isn't customer-facing applications, but internal enterprise systems. We also dive into MCP Apps, developer experience, protocol design, AI tooling, Python, and why building

In this episode, we're joined by Stephen O'Grady, Co-Founder and Principal Analyst at RedMonk, to explore one of the biggest shifts happening in software engineering: AI is making code dramatically cheaper to produce, but everything downstream is becoming the new bottleneck. We discuss why SaaS isn't dead despite the hype, the explosive rise of MCP, why AI agents are overwhelming developer infrastructure, and what happens when every engineer suddenly has dozens of AI developers working alongside them. Stephen explains how package managers, code reviews, security, governance, and enterprise sys