Agentic Conversations (formally mlops.community)

00:20:34

How a Logistics Giant Keeps AI Data Locked Down

Agentic Conversations (formally mlops.community) ·

2026/10/05

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,

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00:44:07

The Caveman Prompting Challenge

Agentic Conversations (formally mlops.community) ·

2026/10/01

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

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00:41:38

AWS Has 16,000 APIs. Can MCP Handle It?

Agentic Conversations (formally mlops.community) ·

2026/09/28

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,

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00:32:30

Skills over MCP on the streets of Tokyo

Agentic Conversations (formally mlops.community) ·

2026/09/24

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

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00:28:49

Walking Tokyo Talking Agent Protocols

Agentic Conversations (formally mlops.community) ·

2026/09/18

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

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00:38:49

Why Cost Per Million Tokens Is A Useless KPI?

Agentic Conversations (formally mlops.community) ·

2026/09/14

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

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00:38:55

The Five-Layer Cake Approach to Scaling AI Without...

Agentic Conversations (formally mlops.community) ·

2026/09/04

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

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00:59:41

The Winchester Mystery House Problem in AI Development

Agentic Conversations (formally mlops.community) ·

2026/08/24

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

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00:29:23

How Predictive Analytics Stops Budget Overruns Before...

Agentic Conversations (formally mlops.community) ·

2026/08/20

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

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00:55:15

How To Delegate To An Agent Like You Would An Employee?

Agentic Conversations (formally mlops.community) ·

2026/08/17

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

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00:32:24

Why Your AI Bill Will Double Before It Gets Better

Agentic Conversations (formally mlops.community) ·

2026/08/03

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

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00:52:27

What an Anthropic Engineer Thinks About MCP

Agentic Conversations (formally mlops.community) ·

2026/07/27

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.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

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00:42:37

AI Hype vs. Real Value

Agentic Conversations (formally mlops.community) ·

2026/07/24

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,

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00:55:11

The Creator of FastMCP Explains the Future of MCP

Agentic Conversations (formally mlops.community) ·

2026/07/20

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

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00:34:37

What Happens When Every Developer Has 20 AI Agents?

Agentic Conversations (formally mlops.community) ·

2026/07/13

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

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00:31:35

AI Agents Should Be Treated Like Hackers

Agentic Conversations (formally mlops.community) ·

2026/07/06

In this episode, we're joined by Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL, to explore what happens when AI agents start interacting with enterprise systems that were never designed for them.We dive into the collision between APIs, MCP, GraphQL, and agentic AI, and why traditional assumptions about trust, permissions, and security are breaking down. Matt argues that AI agents should be treated as untrusted actors by default, and explains why giving agents access to enterprise data

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00:46:52

Developers May Stop Depending on Libraries

Agentic Conversations (formally mlops.community) ·

2026/07/06

In this episode of Agentic Conversations, we're joined by Shaun Smith, software engineer, open source advocate, and contributor at Hugging Face, to explore how AI coding has changed almost overnight.We dive into reinforcement learning, MCP (Model Context Protocol), Fast Agent, Claude Code, open source AI, and why today's language models have become so capable that many traditional software libraries are becoming "liquefied." Shaun explains how reinforcement learning unlocked

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00:22:18

10 Cities. 4 Countries. One Unexpected MCP Lesson.

Agentic Conversations (formally mlops.community) ·

2026/07/06

In this episode, we're joined by Ben Morss, Developer Advocate at DeepL, who spent months traveling across North America and Europe teaching developers about MCP, building MCP servers, and helping teams understand how AI agents actually use tools.We dive into the biggest misconceptions around MCP, why so many developers still misunderstand how it works, and what Ben learned after giving talks and workshops in 10 cities across four countries. Along the way, we explore MCP server design, tool

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00:38:13

The Next Programming Language Is English

Agentic Conversations (formally mlops.community) ·

2026/07/06

In this episode, we're joined by Cornelia Davis, Developer Advocate at Temporal and a longtime software architect who has spent decades helping shape modern cloud-native systems.We explore how programming has evolved from assembly language to cloud-native architectures, and why AI is forcing us to rethink software development once again. Cornelia argues that natural language is becoming a new programming abstraction, while durable execution may be the missing layer that makes AI agents reliable

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00:58:16

Omnigent: Composition, Control, and Collaboration for...

Agentic Conversations (formally mlops.community) ·

2026/07/03

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.In this episode:🍵

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