Agentic Conversations (formally mlops.community)

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 research-led optimization is the only kind that works without degrading the product.Along the way, we cover hidden costs most teams miss, the trade-off between Azure reserved capacity and OpenAI Priority Processing, why fixed subscription pricing is broken in an AI-native world, vendor lock-in risk, and what OpenAI's Guaranteed Capacity announcement really signals about where vendor relationships are heading next.Superhuman: https://superhuman.comJosh Collier: https://www.linkedin.com/in/josh-collier-945b7029/Demetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:[00:00] OpenAI Guaranteed Capacity: what's really going on[01:04] Josh's path into AI FinOps[02:48] Token costs: the 80% price drop[04:16] Why costs will only go up[05:06] External LLMs as financial risk[07:16] Why subscription pricing is dead[08:22] The data residency fee nobody notices[09:33] The cost calculator built in 15 minutes[10:24] How it changed dev team speed[13:00] Tracking costs by service and team[15:33] $400k workflow rebuilt for $80k[17:13] Why only research can optimize tokens[20:00] Speculative decoding win[23:11] One bad query, $40k gone[26:00] Why Azure PTU was exhausting[28:59] Shadow traffic load testing[29:07] Priority processing: no brainer[31:10] Guaranteed capacity: lock-in signal?[32:18] The danger of multi-year AI deals[33:28] Vendor-agnostic proxy as exit strategy

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, developer experience, and how Anthropic thinks about balancing simplicity with long-term flexibility.Along the way, we explore progressive disclosure, tool search, programmatic tool calling, context bloat, forward compatibility, long-running AI tasks, protocol evolution, open-source governance, observability, and why the future of AI infrastructure will depend on designing protocols that can evolve without breaking the ecosystem.Timestamps:[00:00] Introduction[01:59] Why MCP Had to Become Stateless[04:28] The Tradeoffs of Stateless Design[06:13] What We Learned About Agent State[08:04] Sessions, Models & Implicit State[09:33] Migrating to MCP v2[12:19] Lessons from HTTP & Open Source Standards[18:16] Shipping Fast Without Breaking Everything[20:35] The Future Complexity of MCP[22:44] Core Features vs Extensions[26:47] Progressive Disclosure Explained[28:16] Solving Context Bloat[30:50] Why Tool Search Beats Progressive Disclosure[32:10] The Biggest MCP Anti-Pattern[34:25] Designing for Forward Compatibility[38:41] Why "Tasks" Matter[40:53] JSON, Tokens & Better Tool Calling[44:44] Observability & Tracing AI Agents[47:34] Will MCP Ever Be Finished?[50:22] What's Next for MCP

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 EBITDA, and how end-to-end workflow redesign does🧪 Champion vs. challenger — Running a control group against your AI-automated process so ROI is demonstrated, not guessed📞 Why customer care agents are still freaking hard — Context, CDP integration, billing systems, and voice-to-voice latency💸 Tokenomics & FinOps — Consumption-based cost surprises, model selection, prompt engineering, and enforcing cost-per-workflow budgets🔍 Auditing agentic behavior — Using AI to test AI, the missing "SOC 2 for agents," and certifying agents for sensitive use cases👤 Human in the loop as an evolving scale — From reviewing 50% of outputs down to 10% as trust builds🧠 88% do AI, 33% scale it — Building a culture of innovation, and why AI usage is showing up in performance reviews💼 Jobs, reskilling & the operating model reset — Why 75%+ of jobs will be reskilled, not replacedIf you're an AI leader, platform engineer, or exec trying to turn AI experiments into P&L impact, this one's for you.Links & Resources:Connect with Manish: https://www.linkedin.com/in/manishdasaur/PwC AI: https://www.pwc.com/us/en/tech-effect/ai-analytics.htmlPwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.htmlTimestamps:[00:00] AI Hype vs Business Value[00:44] API Spend Tracker Widget[02:18] Tokenomics and FinOps for AI[06:19] Measuring AI Impact Objectively[11:07] AI in Support Workflows[18:05] AI Innovation Culture[27:16] MCP Servers and SOC 2[29:14] Human in the Loop in evolving scale[35:44] AI and Workforce Efficiency[39:59] AI Transformation and Mindset[42:19] Wrap-up

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 great abstractions is often more valuable than exposing more configuration.Along the way, we explore the rapid growth of the MCP ecosystem, how FastMCP became the default way many developers build MCP servers, why "too much magic" can actually hurt developer experience, and what the next generation of AI-powered applications will look like as agents move beyond simple tool calling into rich, interactive experiences.Prefect: https://www.prefect.ioJeremiah Lowin: https://www.linkedin.com/in/jlowinDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:00:00 Lost My Entire Talk00:47 The Story Behind FastMCP02:08 Anthropic Adopted FastMCP02:34 When MCP Took Off04:10 FastMCP vs The Official SDK05:43 Is MCP Actually Dead?06:42 What Everyone Gets Wrong About MCP08:11 MCP's Biggest Use Case10:25 Building Internal AI Systems12:00 Why FastMCP Exploded13:29 Making Complex Software Simple15:10 Can Software Be Too Magical?20:11 MCP Apps Explained23:42 Why Python Needed MCP Apps27:54 The Future of AI Interfaces34:18 AI Should Generate UIs40:11 AI Deleted My Presentation43:30 The AI Assistant We Actually Need48:00 Personal AI vs SaaS52:28 The Future of AI Agents55:06 Final Thoughts

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 systems are all struggling to keep pace with AI-generated software.Along the way, we dive into AI coding tools, MCP adoption, developer productivity, infrastructure scaling, enterprise software, open source, package repositories, governance, and why the hardest problems in software may no longer be writing code—but managing everything that comes after.RedMonk: https://redmonk.comStephen O'Grady: https://www.linkedin.com/in/sogradyDemetrios: https://www.linkedin.com/in/dpbrinkm

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 creates entirely new challenges around governance, access control, and risk management.Along the way, we discuss semantic APIs, enterprise data silos, citizen developers, agent permissions, security boundaries, and how GraphQL and MCP can work together to make enterprise systems more accessible to both humans and AI. The conversation also explores why companies are racing to deploy agents despite the risks, and what the future of enterprise software might look like when AI becomes the primary consumer of APIs.Apollo GraphQL: https://www.apollographql.comMatt DeBergalis: https://www.linkedin.com/in/debergalisAlex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[00:00] AI, APIs, and Trust[01:16] MCP API Lessons[06:16] GraphQL and MCP Integration[12:55] API Security for MCP[16:10] Linux Kernel Security Concerns[19:09] API Design and Controls[21:52] Trust in Autonomous Systems[25:06] MCP GraphQL Wish List[27:13] API Access Patterns[28:44] GraphQL API Perspective

Matt DeBergalis, CTO and Co-Founder of Apollo GraphQL

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 long-running autonomous agents, why ideas are becoming more valuable than code, and how developers should think about building software in an era where AI can generate entire applications.Along the way, we discuss Hugging Face's MCP server, Fast Agent, AI-powered developer tools, multimodal applications, MCP Apps, context windows, coding assistants, Rust, Python, TypeScript, open-weight models, software architecture, and what the future of programming looks like when humans increasingly focus on design instead of implementation.Shaun Smith: https://www.linkedin.com/in/smithshaunDemetrios: https://www.linkedin.com/in/dpbrinkmHugging Face: https://huggingface.co⏱️ Timestamps[00:00] Introduction[01:56] The State of Open Source AI[05:18] Reinforcement Learning Changed Everything[07:50] Fast Agent Explained[10:18] Fast Agent as an MCP Reference Platform[12:20] Building Smarter AI Tools at Hugging Face[15:17] Natural Language Search Instead of APIs[17:46] Why MCP Apps Matter[20:06] The Evolution of MCP Apps[23:05] Building AI-Native User Interfaces[26:12] Context Is the New Programming Language[28:00] The End of Code Libraries[29:50] Why Developers Aren't Writing Code[31:25] AI Changes Software Engineering[33:05] The Future of Open Source AI[35:43] Claude Skills That Save Hours[38:02] Training Models with AI[39:05] Building Your Own AI Tools[40:50] MCP for Consumers, Enterprises, and Developers[43:42] Why Shell Access Makes Agents Smarter[45:18] Secure Agent Workflows[46:08] The Future of AI Interfaces[47:02] Outro#HuggingFace #MCP #OpenSourceAI

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 calling, security concerns, translation workflows, developer education, and how DeepL is using MCP to bring high-quality language translation into AI-powered applications.DeepL: https://www.deepl.comBen Morss: https://www.linkedin.com/in/ben-morss-ph-d-15bab15/Alex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[00:00] AI and API Integration[00:41] DeepL at DevSummit[01:19] MCP Roadshow Origins[03:47] MCP Hackathon Insights[07:52] Security in Model Protocols[10:25] AI Expert vs Noob Queries[16:08] DeepL vs Frontier LLMs[18:16] MCP vs REST API[21:39] MCP Servers and DeepL

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 in production.The conversation dives into probabilistic software, long-running AI agents, MCP tasks, human-in-the-loop workflows, durable timers, distributed systems, and why developers may no longer need to think about infrastructure the way they once did.Cornelia Davis: https://www.linkedin.com/in/corneliadavisDemetrios: https://www.linkedin.com/in/dpbrinkmTemporal: https://temporal.ioTimestamps[00:00] AI Programming Abstractions[00:52] Abstraction Evolution in Programming[04:05] Text to SQL Evolution[10:08] Compensations for Natural Language[12:13] Durable MCP in AI[18:34] Streaming Session Explanation[21:31] Batch Processes with Tasks[29:29] Complexity Relocation in Systems[33:10] Complexity Relocation in Dev[36:36] Programming Model Shifts