Agentic Conversations (formally mlops.community)

The Winchester Mystery House Problem in AI Development

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. CMPND: https://www.cmpnd.ai Drew Breunig: https://www.linkedin.com/in/drewbreunig/ Demetrios: https://www.linkedin.com/in/dpbrinkm Timestamps: [0:00] Cold open: when Claude Code tries to call itself [1:19] Biggest AI news: labs trading diversity for reliability [2:35] How harnesses get trained into models over time [5:41] The problem: your harness starts fighting the model [9:13] When do you need your own harness? [10:02] The Winchester Mystery House warning [16:13] The blank page problem: why everything looks the same [20:50] Infrastructure vs appliances: the thesis lands [22:40] Current tool loadout: GLM, Kimi, Claude Code, Pi [27:04] The Raspberry Pi personal agent running on Slack [31:00] Crystallizing tasks: when to replace AI with pure code [33:10] DSPy explained: separating what from how [35:23] How prompt optimizers actually work [39:31] DSPy pre-dates ChatGPT: model-agnostic programs [44:00] Why you still need to ship the code, not just the spec [50:00] Don't plan more than a month ahead anymore [54:00] Coaching agents all day feels productive — it isn't [57:58] The dopamine of building with agents vs. why you still need human feedback

How Predictive Analytics Stops Budget Overruns Before They Happen?

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?Wayfair: https://www.wayfair.comBrent Eubanks: https://www.linkedin.com/in/brenteubanksDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:[00:00] Intro & teaser [01:14] Meet Brent (FinOps Architect, Wayfair) [02:34] Making AI adoption easy for developers [03:30] Three-area cost governance framework [04:45] Automated FinOps guardrails in pull requests [07:29] Predictive analytics: 400% over budget warnings [08:26] The 400% spender: asset or liability? [10:06] The "spend captain" model [12:13] The goal: 1000x more token use in two years [13:05] The thought experiment [15:08] The $400k workflow rebuilt for $80k [16:42] Hardcoded first, LLM as fallback [19:49] Engineers now own their full cost stack [20:13] The FinOps companion that works overnight [24:10] Guardrails injected before the pull request [26:17] Recurring tasks become automated skills [27:46] Sankey diagram: cost per agent step in real time [28:40] "The bill didn't blow up - that doesn't happen by accident"

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

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. OpenAI: https://openai.com Monaco: https://www.monaco.com Jason Liu: https://www.linkedin.com/in/jxnlco Mihail Eric: https://www.linkedin.com/in/mihaileric Demetrios: https://www.linkedin.com/in/dpbrinkm Timestamps: [00:00] Intro and guest backgrounds [01:36] Why computer use beats plain API calls [09:11] Building an AI-powered morning brief [10:17] Self-monitoring threads that check in on their own [18:26] How OpenAI reviews thousands of PRs a day [19:42] Self-healing pull requests before CI even runs [23:07] Building review skills from teammates' habits [30:52] Why hard work stops being the differentiator [35:03] Introducing the idea of AI deference [42:44] Learning to delegate like hiring your first assistant [46:22] Why voice beats typing for giving agents context [50:56] The Tony Stark Jarvis analogy for this year

Why Your AI Bill Will Double Before It Gets Better

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.com Josh Collier: https://www.linkedin.com/in/josh-collier-945b7029/ Demetrios: https://www.linkedin.com/in/dpbrinkm Timestamps: [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

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MCP Goes Stateless

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

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AI Hype vs. Real Value

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.html PwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html Timestamps: [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

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The Creator of FastMCP Explains the Future of MCP

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.io Jeremiah Lowin: https://www.linkedin.com/in/jlowin Demetrios: https://www.linkedin.com/in/dpbrinkm Timestamps: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

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What Happens When Every Developer Has 20 AI Agents?

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.com Stephen O'Grady: https://www.linkedin.com/in/sogrady Demetrios: https://www.linkedin.com/in/dpbrinkm

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AI Agents Should Be Treated Like Hackers

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.com Matt DeBergalis: https://www.linkedin.com/in/debergalis Alex Salkever: https://www.linkedin.com/in/alexsalkever Timestamps: [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

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Developers May Stop Depending on Libraries

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/smithshaun Demetrios: https://www.linkedin.com/in/dpbrinkm Hugging 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

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10 Cities. 4 Countries. One Unexpected MCP Lesson.

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.com Ben Morss: https://www.linkedin.com/in/ben-morss-ph-d-15bab15/ Alex Salkever: https://www.linkedin.com/in/alexsalkever Timestamps: [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

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The Next Programming Language Is English

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/corneliadavis Demetrios: https://www.linkedin.com/in/dpbrinkm Temporal: https://temporal.io Timestamps [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

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Omnigent: Composition, Control, and Collaboration for AI Agents

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: 🍵 Agents as research partners — Denny uses dueling agents to scout matcha-growing regions in Taiwan, down to soil pH, elevation, and processing infrastructure 🥊 Why agents should debate each other — letting two models argue surfaces the questions you didn't know to ask 🔱 Forking conversations — the missing UX pattern: branch a session, keep the shared context, explore two threads in parallel 🧠 The meta-harness layer — how Omnigent sits above Claude Code, Codex, Pi, and custom agents so models and harnesses become hot-swappable parts 👥 The two-pizza rule for agents — military span-of-control logic says you can manage 5–7 agents before you lose the thread 💸 Tokenomics is the new DevOps — the CapEx→OpEx playbook repeats: give developers spend visibility, keep central governance for the rest 🛡️ Policies, budgets, and guardrails — enforcing cost caps and approval rules at the harness layer instead of inside prompts 🤖 Auto model selection — why classic machine learning (not another LLM) may be the right way to route tasks to cheap vs. frontier models ✍️ "Created by" vs. "assisted by" — the open source accountability debate: whoever submits the code owns the code 🗄️ Databases are back — agents need cheap, stateful memory, which is why Postgres, Lakebase, and serverless databases are having a moment If you're building with coding agents, managing AI spend, or trying to keep up with the harness arms race, this one's for you. Links & Resources: Omnigent (open source): https://www.databricks.com/blog/introducing-omnigent-meta-harness-combine-control-and-share-your-agents Omnigent GitHub: https://github.com/databricks/omnigent Denny Lee on LinkedIn: https://www.linkedin.com/in/dennyglee Denny's blog: https://dennyglee.com Tokenomics Foundation announcement: https://www.finops.org/insights/finops-x-2026-day-1-keynote/ Timestamps: [00:00] SOA to LLMOps Transition [01:06] Agentic Research Workflow [10:45] Agent Debate for Execution [13:53] Agentic Footnote Concept [24:41] Harnesses in Agent Systems [32:43] Harnessing Multi-Layered Agents [38:06] Token Spending Awareness [41:01] Token Spend Efficiency [43:53] Model Selection Frustration [51:06] Meta Harness in AI [53:15] Harness Layers Model [57:17] Wrap up #Tokenomics #AIAgents #Omnigent

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The Current State of Agentic Retrieval - Qdrant Roundtable

Qdrant Roundtable episode: The Current State of Agentic Retrieval Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps GPU Guide: https://go.mlops.community/gpuguide Big shout-out to Qdrant for the collaboration! // Abstract AI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures. As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time. If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems. // Bio Ewa Szyszka Ewa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work. Dylan Couzon Dylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what he’s building and learning along the way. Neil Kanungo Neil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan. Evgeniya Sukhodolskaya Developer Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Master’s in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies. Andrei Cristea Andrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Master’s degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering. Hosted by Demetrios // Related Links Website: https://qdrant.tech/ ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~ Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExplore Join our Slack community [https://go.mlops.community/slack] Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register] MLOps Swag/Merch: [https://shop.mlops.community/]

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AI Agents in Healthcare?

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. In this conversation:🏗️ The four-layer architecture — Dumb data at the bottom, then access permissions, then MCP, then reasoning agents on top. Why logic never touches the data layer.🔐 MCP vs REST — Why enforcing per-role compliance in a REST API meant encoding permissions everywhere, and how MCP collapses that mess.🪪 HIPAA, auditability & traceability — Proving a specific person (not a snooping agent) accessed a record, with a full audit trail that regulators actually accept.🎟️ The nightclub-bouncer analogy — How MCP reorganizes the entire "club" per guest instead of just checking a VIP list.⌚ Wearables & real-world data — Turning an Apple Watch arrhythmia signal into a triaged, severity-scored workflow with doctors in the loop.🧭 Deterministic vs model-driven — Why anything clinical or regulatory stays binary, and the agent-as-coach (not decision-maker) pattern for patients.🛑 Keeping agents on the leash — Tool restriction, behavioral metadata, and drift/anomaly detection so an agent can't reinterpret its own job.⚡ The instant kill switch — Revoke permission, and the agent returns a hard 404, never partial data.⚖️ The liability question — When an agent follows a designed workflow and something goes wrong, who's responsible: patient, host, or provider? The industry hasn't decided.📋 Kingsley's MCP wishlist — Built-in traceability (OTEL-style spans), native time-bound enforcement, and guardrails against agent-to-agent data leakage.If you're building agentic systems for healthcare, finance, legal, or any regulated industry where "the agent did it" isn't a good enough answer — this one's for you.Links & Resources🔗 HealID — https://gethealid.com/🔗 Kingsley Madikaegbu — https://www.linkedin.com/in/kmadikaegbu🔗 Alex Salkever / Agentic AI Foundation — linkedin.com/in/alexsalkever🔗 MCP Dev Summit North America — https://events.linuxfoundation.org/mcp-dev-summit-north-america/Timestamps:[00:00] Intro[00:13] AI Agent Liability[01:10] MCP in Healthcare AI[06:30] MCP vs REST Architecture[11:29] Healthcare Integration Challenges[18:29] Non-compliant Patient Challenges[24:13] Deterministic vs Model-Driven Workflows[28:08] AI in Healthcare Conversations[34:38] Agent-to-agent workflows in healthcare[38:02] Future MCP security

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Coding Agents Are Secretly General Agents

In this episode: 🧠 Coding agents are generalist agents — why "positive transfer" means an agent that's better at code is better at everything, and how that makes them "AGI-complete" ⏳ "Code will be solved in a year" — what the automation of knowledge work actually looks like, and why Jay joined ClickUp to be on it 🏗️ Why the labs are crushing AI startups — free-for-two-years deals, Windsurf losing Claude access, and the brutal economics of building on top of frontier models 🔗 The real moat is convergence — context, surfaces, and unit economics, a.k.a. "Cursor for your whole job" 💬 Slack's data walls & the Glean problem — why fragmentation is the enemy and a single system of record wins 🧪 RLVR & verifiability — why code became the perfect training ground for agents, and how to tell if you're even getting better 🔬 LLMs are running the frontier of science — Putnam 12/12, Erdős problems, simulating a cell, and vibe-writing economics papers 🚗 The car wash test that still breaks GPT-5 — spiky models, world models, Plato's cave, and the "stochastic parrot" debate 🏖️ Plus: mechanistic interpretability as "brain surgery," catastrophic forgetting, the danger of deleting knowledge from models, and a pitch for a "resort for LLMs" Whether you're building agents, leading an AI team, or just trying to figure out what "agentic" really means for everyday work — this one's a fun, deep ride. 🔗 Links & Resources Jay Hack: linkedin.com/in/jayhack ClickUp: clickup.com MLOps Community: go.mlops.community Mentioned: Gödel, Escher, Bach (Douglas Hofstadter) · "Machine Learning: The High-Interest Credit Card of Technical Debt" (Sculley et al.) · Periodic Labs · Ginkgo Bioworks · Physical Intelligence ⏱️ Timestamps [00:00] AI Timeline [00:22] AI Startups and Timing [06:30] GPT-3 Impact [13:24] Selling CodeGen to ClickUp [19:31] AI Interaction Patterns [28:41] Slack and AI Agents [36:11] ClickUp Task Automation [41:32] AI in Scientific Research [48:48] Human Understanding vs AI [54:18] Catastrophic Forgetting Explained [59:59] AI Delegating to Humans [1:05:00] Agent-Based Game Integration [1:08:42] LLM vs Game Design [1:11:27] Wrap up #AIAgents #AgenticAI #ClickUp

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The Dark Side of MCP Servers

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. What we get into:🔓 OAuth on MCP — Why the spec adopted OAuth as its authorization standard, and the class of spoofing attacks it shuts down.☠️ Tool poisoning — How a malicious server hides instructions in tool descriptions, and why your agent trusts them by default.🧪 MCP Debugger & ToolBench — Shining a light on the rough edges by grading servers from S-tier to F-tier.🖥️ Sandboxing agents — Giving an agent a shell and a file system without handing over the keys to your machine.📜 Allow lists — Why MCP has client-level allow lists but skills mostly don't — and why that worries them.🔄 The auto-update problem — How skills and servers that silently update become a supply-chain risk ("rug pulls").✅ SOC 2, honestly — Why the controls are voluntary, misunderstood, and actually about best practices.🤖 AI-generated PRs — The new behaviors to watch for as agents start writing and merging code. If you build agents, ship MCP servers, or are responsible for AI security at your company, this one's for you. 🔗 Links & ResourcesArcade.dev: https://www.arcade.devArcade MCP framework (GitHub): https://github.com/ArcadeAI/arcade-mcpSam Partee (GitHub): https://github.com/sparteeNate Barbettini (LinkedIn): https://www.linkedin.com/in/nbarbettiniMLOps.community: https://mlops.community ⏱️ Timestamps[00:00] Skills, agents, and local context [08:36] MCP Debugger grades your server [10:34] Why AI clients are still buggy [20:54] Why agents shouldn’t always have shell access [22:44] “I have a spicy take.” [26:27] “Do not build your own auth.” [31:14] The “checking someone else’s email” problem [35:40] “OAuth is the best worst option.” [43:50] The future of AI entertainment [46:19] Tool poisoning explained [50:49] “Trust me, bro,” is not a security solution [52:45] MCP registries as the App Store model [1:00:28] AI-generated PRs and speed vs quality [1:02:37] Why behavior-driven development is coming back [1:08:11] Have we already reached AGI? #MCP #AIAgentSecurity #ToolPoisoning

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Sandboxing, Agent Harnesses, and Agent Teamwork

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. In this episode: 🔧 The on-call problem — Why one broken service still drags ten engineers onto a call, and how AI changes that 🤖 What an AI SRE actually is — How Cleric investigates across your existing observability stack instead of adding another tool 🧠 Learning over MTTR — Why Shahram argues the value isn't alert triage, it's an agent that gets better every investigation 🪜 Ramping like a new engineer — Explore the environment, learn from the work, talk to the team 🔁 The investigate–measure–learn loop — Turning what worked on one incident into context for the next 🕸️ Knowledge graphs & operational memory — Mapping teams, clusters, and dependencies so insight from one team helps another ⚡ Under two minutes to root cause — What "fast" really requires in a live production environment 🚀 The road to autonomy — From assisted investigation toward self-healing infrastructure If you're an SRE, platform engineer, DevOps lead, or anyone building or buying AI agents for production, this one's for you. 🔗 Links & Resources Cleric: https://cleric.ai Shahram on LinkedIn: https://www.linkedin.com/in/shahramanver/ Willem Pienaar (Co-Founder/CTO): https://www.linkedin.com/in/willempienaar/ Cleric launches the first self-learning AI SRE: https://cleric.ai/blog/cleric-launches-the-first-self-learning-ai-sre MLOps Community: https://mlops.community Join the community: https://go.mlops.community/slack ⏱️ Timestamps [00:00] Tech Jargon Confusion [00:27] Harness vs Model [08:48] Model Evolution in Cleric [13:36] Sandboxing and Simulated Environments [20:40] Shifting AI Perceptions [24:10] Managing Humans vs Agents [31:32] Steering Parallel Agents [34:16] Human Decision Integration in Models [43:28] 80/20 Data Split [49:40] Becoming a Skill [53:35] 2027 Agent Autonomy [59:14] Agent Learning in Production [1:04:31] Software as Personal Capabilities [1:08:31] Vibe Coding vs Durability [1:18:23] Wrap up #AISRE #SiteReliabilityEngineering #AIAgents

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Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + Chronon

Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + ChrononJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to ZiplineAI for the collaboration!// AbstractReal-time ML use cases like personalization and risk decisioning come with a unique set of challenges: serving fresh feature values at low latency for inference, generating temporally consistent backfills for training, and building complex chains of on-demand, batch, and streaming transformations. In this roundtable, practitioners from Intuit, CreditKarma, Depop, and OpenAI share how they use Zipline and the OSS Chronon project to solve these challenges and deploy real-time ML use cases in production.// BioGerman KrikorianGerman is a Software Engineer on the Feature Platform team at Credit Karma. Since joining the company during the early development of its recommendation system, they have played a key role in building and scaling the platform over the years. Their work focuses on feature pipelines and the feature store, which serves as critical infrastructure supporting numerous teams and business verticals across the organization.Ben MagyarBen is an engineer at Depop working on ML and data systems. Before Depop, he worked on Search at Etsy. Most of his work is around the infrastructure and operational problems that come with running ML systems at scale.Raj KatakamRaj architects ML Infrastructure at Credit Karma (Intuit). He holds a Master's in Software Engineering from Carnegie Mellon and a B.Tech in EECE from IIT Kharagpur. His interests include ML Infrastructure, Distributed Systems, Real-Time Data Processing, and Generative AI. His current focus is on providing feature engineering platforms, production GenAI infrastructure, vector databases, ML model serving, and MLOps pipelines for fraud detection, personalized recommendations, financial insights, and model explainability.Mick JermsurawongLed Flyte ML training/experimentation at Stripe, and now led Chronon for ML features at OpenAIHosted by Demetrios// Related LinksWebsite: https://zipline.ai/https://chronon.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with German on LinkedIn: /e2zdkwh8cxghydg/Connect with Raj on LinkedIn: /rajkiran2190Connect with Mick on LinkedIn:/mick-jermsurawong/

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MCP Servers Are Becoming the UI for AI Agents

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. What we get into: 🐱 What MCPcat Is — Open-source analytics and live debugging built specifically for MCP servers 🎬 Session Replay — Watch an agent's full journey through your server, tool call by tool call 🎯 Agent Intent & Goals — Understand "why" a tool was called, not just that it was 🔍 Trace Debugging — Find exactly where agents and users get stuck or confused 🚨 Catching Hallucinations — How issue tracking surfaces when an LLM goes off the rails 🔒 Privacy-First by Design — Client-side redaction so sensitive data never leaves your environment ⚡ One-Line Integration — Python, TypeScript, and Go SDKs that drop into existing stacks 📊 Works With Your Stack — Native support for OpenTelemetry, Datadog, and Sentry 🚀 The Future of MCP — Where agent observability and the MCP ecosystem are heading If you build, ship, or maintain MCP servers — or you're trying to figure out why your AI agents misbehave in production — this one's for you. 🔔 Subscribe, like, and share for more conversations on agentic AI: ▶️ YouTube: https://www.youtube.com/@AAIFAgenticConversations🎧 Spotify: https://open.spotify.com/show/033rZZJrQOVSSmhcStFhZA?si=rUNjFuNqRvGvAEWwqms7TA Links & Resources: 🐱 MCPcat: https://mcpcat.io 💻 MCPcat on GitHub: https://github.com/mcpcat 👤 Naseem on LinkedIn: https://www.linkedin.com/in/naseem-al-naji 🐙 Naseem on GitHub: https://github.com/naji247 Timestamps: [00:00] Intro [01:41] MCP Needs Gatekeepers [06:32] Measuring MCP Success [13:57] MCPAT Feature Rollouts [18:50] MCP Server Query Optimization [26:48] UI Design Shift [29:14] MCP Server Design Choices [33:51] User Journey Traceability [40:40] Agent Experience Evaluation [45:23] AI Model Improvement Strategies #MCP #AIAgents #Observability

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