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/alexsalkever

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.coTimestamps:00:00 Introduction01:56 The State of Open Source AI05:18 Reinforcement Learning Changed Everything07:50 Fast Agent Explained10:18 Fast Agent as an MCP Reference Platform12:20 Building Smarter AI Tools at Hugging Face15:17 Natural Language Search Instead of APIs17:46 Why MCP Apps Matter20:06 The Evolution of MCP Apps23:05 Building AI-Native User Interfaces26:12 Context Is the New Programming Language28:00 The End of Code Libraries29:50 Why Developers Aren't Writing Code31:25 AI Changes Software Engineering33:05 The Future of Open Source AI35:43 Claude Skills That Save Hours38:02 Training Models with AI39:05 Building Your Own AI Tools40:50 MCP for Consumers, Enterprises, and Developers43:42 Why Shell Access Makes Agents Smarter45:18 Secure Agent Workflows46:08 The Future of AI Interfaces47:02 Outro

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

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 momentIf 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-agentsOmnigent GitHub: https://github.com/databricks/omnigentDenny Lee on LinkedIn: https://www.linkedin.com/in/dennygleeDenny's blog: https://dennyglee.comTokenomics 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

Qdrant Roundtable episode: The Current State of Agentic RetrievalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Qdrant for the collaboration!// AbstractAI 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.// BioEwa SzyszkaEwa 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 CouzonDylan 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 KanungoNeil 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 SukhodolskayaDeveloper 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 CristeaAndrei 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 LinksWebsite: https://qdrant.tech/~~~~~~~~ ✌️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/]

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

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 & ResourcesJay Hack: linkedin.com/in/jayhackClickUp: clickup.comMLOps Community: go.mlops.communityMentioned: 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

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

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 infrastructureIf you're an SRE, platform engineer, DevOps lead, or anyone building or buying AI agents for production, this one's for you.🔗 Links & ResourcesCleric: https://cleric.aiShahram 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-sreMLOps Community: https://mlops.communityJoin 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