Afleveringen
-
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
-
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/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
-
Zijn er afleveringen die ontbreken?
-
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-15bab15Alex Saltkever: 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
-
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/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
-
Denny Lee is PM Director, Startups & Ecosystem at Databricks, a longtime Apache Spark, MLflow, and Delta Lake contributor β 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:
π΅ Agents as research partners β Denny uses dueling agents to scout matcha-growing regions in Taiwan, down to soil pH, elevation, and processing infrastructure
π₯ Why agents should debate each other β letting two models argue surfaces the questions you didn't know to ask
π± Forking conversations β the missing UX pattern: branch a session, keep the shared context, explore two threads in parallel
π§ The meta-harness layer β how Omnigent sits above Claude Code, Codex, Pi, and custom agents so models and harnesses become hot-swappable parts
π₯ The two-pizza rule for agents β military span-of-control logic says you can manage 5β7 agents before you lose the thread
πΈ Tokenomics is the new DevOps β the CapExβOpEx playbook repeats: give developers spend visibility, keep central governance for the rest
π‘οΈ Policies, budgets, and guardrails β enforcing cost caps and approval rules at the harness layer instead of inside prompts
π€ Auto model selection β why classic machine learning (not another LLM) may be the right way to route tasks to cheap vs. frontier models
βοΈ "Created by" vs. "assisted by" β the open source accountability debate: whoever submits the code owns the code
ποΈ Databases are back β 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/
-
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 heβs 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 Masterβs 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 Masterβs 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/
~~~~~~~~ βοΈConnect With Us βοΈ ~~~~~~~
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/]
-
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 β without breaching HIPAA, leaking PHI, or letting an agent quietly go off the rails. In this conversation:ποΈ The four-layer architecture β Dumb data at the bottom, then access permissions, then MCP, then reasoning agents on top. Why logic never touches the data layer.π MCP vs REST β Why enforcing per-role compliance in a REST API meant encoding permissions everywhere, and how MCP collapses that mess.πͺͺ HIPAA, auditability & traceability β Proving a specific person (not a snooping agent) accessed a record, with a full audit trail that regulators actually accept.ποΈ The nightclub-bouncer analogy β How MCP reorganizes the entire "club" per guest instead of just checking a VIP list.β Wearables & real-world data β Turning an Apple Watch arrhythmia signal into a triaged, severity-scored workflow with doctors in the loop.π§ Deterministic vs model-driven β Why anything clinical or regulatory stays binary, and the agent-as-coach (not decision-maker) pattern for patients.π Keeping agents on the leash β Tool restriction, behavioral metadata, and drift/anomaly detection so an agent can't reinterpret its own job.β‘ The instant kill switch β Revoke permission, and the agent returns a hard 404, never partial data.βοΈ The liability question β When an agent follows a designed workflow and something goes wrong, who's responsible: patient, host, or provider? The industry hasn't decided.π Kingsley's MCP wishlist β 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 β this one's for you.Links & Resourcesπ HealID β https://gethealid.com/π Kingsley Madikaegbu β https://www.linkedin.com/in/kmadikaegbuπ Alex Salkever / Agentic AI Foundation β linkedin.com/in/alexsalkeverπ MCP Dev Summit North America β 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:
π§ Coding agents are generalist agents β why "positive transfer" means an agent that's better at code is better at everything, and how that makes them "AGI-complete"
β³ "Code will be solved in a year" β what the automation of knowledge work actually looks like, and why Jay joined ClickUp to be on it
ποΈ Why the labs are crushing AI startups β free-for-two-years deals, Windsurf losing Claude access, and the brutal economics of building on top of frontier models
π The real moat is convergence β context, surfaces, and unit economics, a.k.a. "Cursor for your whole job"
π¬ Slack's data walls & the Glean problem β why fragmentation is the enemy and a single system of record wins
π§ͺ RLVR & verifiability β why code became the perfect training ground for agents, and how to tell if you're even getting better
π¬ LLMs are running the frontier of science β Putnam 12/12, ErdΕs problems, simulating a cell, and vibe-writing economics papers
π The car wash test that still breaks GPT-5 β spiky models, world models, Plato's cave, and the "stochastic parrot" debate
ποΈ 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 β this one's a fun, deep ride.
π Links & Resources
Jay Hack: linkedin.com/in/jayhack
ClickUp: clickup.com
MLOps Community: go.mlops.community
Mentioned: GΓΆdel, Escher, Bach (Douglas Hofstadter) Β· "Machine Learning: The High-Interest Credit Card of Technical Debt" (Sculley et al.) Β· Periodic Labs Β· Ginkgo Bioworks Β· Physical Intelligence
β±οΈ 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 β and how to ship agents safely anyway.
What we get into:π OAuth on MCP β Why the spec adopted OAuth as its authorization standard, and the class of spoofing attacks it shuts down.β οΈ Tool poisoning β How a malicious server hides instructions in tool descriptions, and why your agent trusts them by default.π§ͺ MCP Debugger & ToolBench β Shining a light on the rough edges by grading servers from S-tier to F-tier.π₯οΈ Sandboxing agents β Giving an agent a shell and a file system without handing over the keys to your machine.π Allow lists β Why MCP has client-level allow lists but skills mostly don't β and why that worries them.π The auto-update problem β How skills and servers that silently update become a supply-chain risk ("rug pulls").β SOC 2, honestly β Why the controls are voluntary, misunderstood, and actually about best practices.π€ AI-generated PRs β 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.
π 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
β±οΈ 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 shouldnβt always have shell access
[22:44] βI have a spicy take.β
[26:27] βDo not build your own auth.β
[31:14] The βchecking someone elseβs emailβ problem
[35:40] βOAuth is the best worst option.β
[43:50] The future of AI entertainment
[46:19] Tool poisoning explained
[50:49] βTrust me, bro,β 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 β 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 β it's an agent that *learns* and compounds operational memory across your whole org.
In this episode:
π§ The on-call problem β Why one broken service still drags ten engineers onto a call, and how AI changes that
π€ What an AI SRE actually is β How Cleric investigates across your existing observability stack instead of adding another tool
π§ Learning over MTTR β Why Shahram argues the value isn't alert triage, it's an agent that gets better every investigation
πͺ Ramping like a new engineer β Explore the environment, learn from the work, talk to the team
π The investigateβmeasureβlearn loop β Turning what worked on one incident into context for the next
πΈοΈ Knowledge graphs & operational memory β Mapping teams, clusters, and dependencies so insight from one team helps another
β‘ Under two minutes to root cause β What "fast" really requires in a live production environment
π The road to autonomy β 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.
π 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
β±οΈ 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
-
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/~~~~~~~~ βοΈConnect With Us βοΈ ~~~~~~~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/
-
Naseem Al-Naji is the co-founder of MCPcat.io and the creator of Opal β 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:
π± What MCPcat Is β Open-source analytics and live debugging built specifically for MCP servers
π¬ Session Replay β Watch an agent's full journey through your server, tool call by tool call
π― Agent Intent & Goals β Understand "why" a tool was called, not just that it was
π Trace Debugging β Find exactly where agents and users get stuck or confused
π¨ Catching Hallucinations β How issue tracking surfaces when an LLM goes off the rails
π Privacy-First by Design β Client-side redaction so sensitive data never leaves your environment
β‘ One-Line Integration β Python, TypeScript, and Go SDKs that drop into existing stacks
π Works With Your Stack β Native support for OpenTelemetry, Datadog, and Sentry
π The Future of MCP β Where agent observability and the MCP ecosystem are heading
If you build, ship, or maintain MCP servers β or you're trying to figure out why your AI agents misbehave in production β this one's for you.
π Subscribe, like, and share for more conversations on agentic AI:
βΆοΈ YouTube: https://www.youtube.com/@AAIFAgenticConversationsπ§ Spotify: https://open.spotify.com/show/033rZZJrQOVSSmhcStFhZA?si=rUNjFuNqRvGvAEWwqms7TA
Links & Resources:
π± MCPcat: https://mcpcat.io
π» MCPcat on GitHub: https://github.com/mcpcat
π€ Naseem on LinkedIn: https://www.linkedin.com/in/naseem-al-naji
π 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
-
Stanislas Polu is Co-Founder & CTO of Dust β the enterprise AI agent platform used by 51,000 workers at 3,000+ companies. Before Dust, he spent three years on OpenAI's research team under Ilya Sutskever, working on mathematical reasoning in language models, and prior to that was an engineer at Stripe. He brings a rare combination of frontier AI research and product-building experience to the enterprise agent space.
Agents & the $40M Bet on Multiplayer AI // MLOps Podcast #384 with Stanislas Polu, Co-Founder & CTO of Dust
π€ What is Dust? β How Dust enables teams to build and deploy AI agents powered by internal company data, and why the "multiplayer AI" model is winning in enterprise.
π§ From OpenAI Research to Startup Founder β Stanislas's journey from studying mathematical reasoning in LLMs under Ilya Sutskever to co-founding an enterprise AI company in Paris with Gabriel Hubert.
π The $40M Series B β What Dust is building with fresh funding, the bet on human-agent collaboration as the future of work, and what "multiplayer AI" actually means in practice.
π The Outer-Loop Era β Stanislas's framework for thinking about where AI agents create the most value: not just automating tasks, but rewiring how work gets done across entire organizations.
β οΈ What Most Enterprise AI Gets Wrong β The biggest mistakes companies make when deploying AI agents, why adoption fails, and how Dust achieves 70%+ weekly adoption rates.
π Building Reliable Agent Infrastructure β Lessons from scaling to thousands of companies: observability, governance, data security, and why enterprise AI is harder than it looks.
π οΈ Horizontal vs. Vertical AI Platforms β Why Dust chose to build a horizontal enterprise agent platform and how that decision shapes product, go-to-market, and technical architecture.
This episode is essential for AI/ML engineers, enterprise AI leads, and anyone building or deploying AI agents at scale inside organizations.
π Links & Resources:
β’ Dust: https://dust.tt
β’ Stanislas Polu on X/Twitter: https://x.com/spolu
β’ Dust on LinkedIn: https://www.linkedin.com/company/dust-tt
β’ Dust $40M Series B announcement: https://dust.tt/blog
β’ "The Outer-Loop Era" talk by Stanislas (dotconferences): https://www.youtube.com/watch?v=_outer_loop
β’ Dust + Stripe MCP integration: https://stripe.com/customers/dust
β’ Dust + Datadog observability case study: https://datadoghq.com/case-studies/dust
β±οΈ Timestamps
[00:00] Future of Work
[00:19] Dust Scaling Lessons
[04:44] Human-Agent Collaboration
[14:24] Pod as Workspace
[22:30] Work Flow Optimization
[29:37] Multiplayer Collaboration Vision
[39:55] Token Economics and Inference
[47:20] AI Pricing Challenges
[52:36] Dust vs Co-work
[57:06] Agentic Work Infrastructure
[1:04:23] Stateful Sandbox Challenges
[1:09:58] Product Use Case Discussion
[1:14:05] Agent Data Interaction Needs
[1:20:09] Wrap up
#EnterpriseAI #AIAgents #Dust
-
James Everingham is the CEO and Co-founder of Guild.ai β the AI agent control plane for production teams. With roots at Netscape, Instagram (Head of Engineering), and Meta (Head of Dev Infra, leading a 1,000-person org), James brings rare, hard-won expertise to the challenge of operating AI agents at scale.
From Single-Player to Multi-Player: Operating AI Agents at Scale // MLOps Podcast #383 with James Everingham, CEO and Co-founder of Guild.ai
In this episode, James unpacks what actually breaks when you move from a single AI agent to a fleet of them β and what engineering leaders need to build before it's too late.
π― Single-Agent vs. Multi-Agent Systems β Why "single-player" AI workflows don't survive contact with production reality, and what the shift to multi-agent coordination actually demands from your infrastructure.
π The Agent Control Plane β What it is, why every engineering org needs one in 2026, and how Guild.ai is building the neutral layer to deploy, govern, and share agents across any framework or model.
β οΈ Non-Determinism at Scale β Why AI agents behave like employees, not software, and why you need workforce-style governance β not just observability tooling β to manage them.
πΈ Token Spend & Cost Visibility β How teams running agents in production are flying blind on cost, and what Guild shows you that your current stack doesn't.
ποΈ Lessons from Meta's DevMate β How Meta's AI coding agent went from experiment to submitting 50% of all diffs, and what that journey teaches every engineering leader about scaling agents safely.
π¦ Agent Identity & Governance β Why every agent needs an identity, what happens when they don't have one, and how agent sprawl becomes a governance crisis fast.
π Sharing Agents as Infrastructure β Why Guild treats agents as shared production infrastructure rather than one-off scripts, and how that changes the economics of AI investment.
π οΈ Framework Agnosticism β Why betting on a single agent framework is a losing strategy, and how to build for a multi-model, multi-framework world from day one.
Essential viewing for engineering leaders, AI platform teams, and founders building production-grade agentic systems.
π Guild.ai: https://guild.ai
π James on X/Twitter: https://x.com/jevering
π James on LinkedIn: https://www.linkedin.com/in/jameseveringham
π Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
β±οΈ Timestamps
[00:00] Context Transfer Challenges
[00:51] Control Plane for Agents
[02:17] Effective Agent Policies
[09:23] Agent Governance Policies
[15:34] Developer Tool Adoption
[22:02] Knowledge Sharing and Open Source
[24:59] Simulated Deployments and Confidence
[29:36] Agent Workloads vs Human Workloads
[39:55] AI as a Customer
[47:59] Agent Hub vs Autonomy
[53:21] Wrap up
#AgenticAI #AIAgents #AIEngineering
-
Thiago Cardoso is the Director of Data & AI at iFood and the architect behind iFood Pago's AI agent platform. This fintech system serves millions of restaurants across Brazil through WhatsApp and the iFood app. In this episode, he breaks down what it actually takes to ship agentic AI in production at scale.
The Control-vs-Magic Spectrum Building Agents // MLOps Podcast #382 with Thiago Cardoso, Director of Data & AI at iFood
π€ WHAT WE COVER:
πΉ Control vs. Magic β Thiago's spectrum model for thinking about AI agents, from deterministic pipelines to fully autonomous systems
πΉ iFood Pago Explained β How iFood's embedded fintech arm uses AI agents to provide credit, loans, and financial services to restaurants
πΉ WhatsApp as an AI Interface β Why WhatsApp is the primary channel for merchant interactions in Brazil and how agents are deployed there
πΉ Multi-Agent Architecture β Why single monolithic agents break down and how to split them into sub-graphs with specialized contexts and tool sets
πΉ Context Engineering β Why what you put in the agent's context window is more important than the model itself
πΉ Human-in-the-Loop Design β How to build trust with merchants while minimizing friction in agentic workflows
πΉ LangGraph in Production β How Thiago's team uses LangGraph to build stateful, multi-agent pipelines
πΉ Debugging with AI β Generating on-the-fly HTML/JavaScript visualization tools to investigate data pipeline problems
πΉ The Cost of Software Going to Zero β What happens to demand when software becomes nearly free to build
πΉ Personalization at Scale β Serving millions of restaurants with AI that knows their business context
π― This episode is for AI engineers, ML practitioners, and fintech builders who want to understand what production agentic AI looks like beyond the demos.
π LINKS & RESOURCES:
Thiago Cardoso on LinkedIn: https://www.linkedin.com/in/thiagoncc/
iFood: https://www.ifood.com.br
iFood Pago: https://ifoodpago.com.br
ZenML iFood Case Study: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agent
LangGraph: https://www.langchain.com/langgraph
β±οΈ TIMESTAMPS
[00:00] Control vs Magic in AI
[00:18] Foodpago Fintech Ecosystem
[08:59] Scaling Personalization with AI
[15:04] Chat UI Evolution
[20:22] Context Layer in Systems
[26:39] Agent Growth Dynamics
[33:39] Job Evolution with Open Claude
[39:54] AI and Software Costs
[41:50] Wrap up
#AIAgents #Fintech #iFood
-
Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company β now he's back, betting that logs are all you need to replace Datadog.
Logs Are All You Need: Rethinking Observability with AI Agents // MLOps Podcast #381 with Sherwood Callaway, the Founder of Sazabi
π What's covered:
πͺ΅ Logs vs. The Three Pillars β Sherwood makes the case that the traditional observability stack (metrics, logs, traces) is overkill. In 2026, with AI agents in the loop, logs alone are sufficient β and dramatically simpler to instrument.
π¨ AI-Generated Alerts, Not AI-Evaluated Alerts β Instead of using AI to triage your noisy alert stream, Sazabi generates the alerts autonomously from your logs and codebase β so you never configure a monitor again.
π€ Agent Sandboxing & Bash Access β How Sazabi gives its AI agent a persistent bash sandbox with CLI tool access, why every other action routes through that sandbox, and how RLS database permissions keep the agent from doing damage.
π§ Agentic Memory via Git β Sazabi's novel approach to persisting agent memory across threads using Git branches β enabling multiple parallel sub-agents to share findings without bloating the context window.
π Multi-Agent Parallelization β How Sazabi spawns sub-agents and background agents on-demand to investigate production issues in parallel, the way Claude Code displays a live to-do list of agent work.
π Why Evals Are Hard (and What They Built Instead) β An honest conversation about the difficulty of evaluating agentic systems, log-based eval proxies, and why Sazabi still doesn't buy third-party eval tooling.
β‘ MCP Servers, Skills Bloat & Context Management β The tradeoffs between MCP servers and local skill files, progressive tool disclosure, and why context window management is the hidden bottleneck in production agent systems.
π― Building a Moat in 2026 β Sherwood and Demetrios debate what a defensible advantage actually looks like when every AI tool can be cloned fast. Spoiler: "We built it first" is not a moat.
π Beta Launch & Who It's For β Sazabi is in closed beta and opening the waitlist. If your team uses Cursor or Claude Code and you have production traffic you can't afford to break, this is built for you.
π Perfect for: AI engineers, SREs, DevOps teams, and founders building production-grade agent systems who are questioning whether their current observability stack is overbuilt.
π Links & Resources
π Sazabi: https://sazabi.com
π Sazabi on Y Combinator: https://www.ycombinator.com/companies/sazabi
πΌ Sherwood Callaway on LinkedIn: https://www.linkedin.com/in/sherwood-callaway
π° SiliconANGLE coverage: https://siliconangle.com/2026/04/08/startup-sazabi-bets-on-logs-and-ai-agents-to-replace-traditional-observability-stacks/
π» MLOps.community: https://mlops.community
β±οΈ Timestamps
[00:00] Genetic Agent Evolution
[00:33] Dethroning Datadog
[03:13] Sazabi vs Traditional Observability
[10:47] MCP vs CLI Paradigm
[15:12] Sandbox Usage for Agents
[24:28] Genetic Prompt Optimization
[32:34] Eval and Agent Spawning
[38:45] RL Environment Tensions
[45:40] Sazabi is hiring!
[46:10] Wrap up
#Observability #AIAgents #DevTools
-
Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE β the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.
AI Is Fast. AI Projects Are Slow. Let's Fix That. // MLOps Podcast #378 with JRocketRide's Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect)A huge shout-out to β¨RocketRideβ© for this collaboration!
π What's covered:
ποΈ Why AI infrastructure needs standardization β how coding agents produce inconsistent "glue code" across projects and why a typed node graph fixes it
β‘ Efficiency AI vs. Opportunity AI β the two paths companies take with generative AI, and which one actually compounds growth
π Multi-agent pipeline orchestration β running CrewAI, LangChain, and DeepAgent side-by-side to benchmark which works best for your use case
π° Cutting LLM costs in half β design-time strategies for routing tasks to cheaper models without sacrificing output quality
π Pipeline observability & debugging β logging every node step in dev and production so you can pinpoint exactly where a 10-step pipeline breaks
πΌοΈ Beyond text: image, video & audio nodes β frame grabbing, OCR, Whisper transcription, and speech-to-text running on shared GPU infrastructure
π RocketRide Cloud β one-click deploy from local to cloud with dynamic GPU scaling and cost-efficient shared inference
π§ Intentionality in agentic development β why moving fast with AI agents creates "crappy code fast" and how skills/context files change the equation
π MCP support & framework-agnostic design β swap any model, tool, or framework without rewritesThis episode is essential for AI engineers, ML practitioners, and developers building production LLM applications who want to stop reinventing infrastructure and start shipping.
π Links & Resources:
β’ RocketRide website: https://rocketride.ai
β’ RocketRide open source (GitHub): https://github.com/rocketride-org/rocketride-server
β’ AIDE VS Code Extension: https://rocketride.org
β’ MLOps Community: https://mlops.community
β’ Discord: https://discord.gg/Hd4PukFt2H
β±οΈ Timestamps
[00:00] Cost Savings in AI
[00:21] AI, Developer, and Software Development Evolution
[02:51] Intentionality in Software Development
[10:51] Model Skill Optimization
[17:08] Primitives in AI Systems
[29:00] Coding Agent Challenges
[37:09] RocketRide Inspiration
[44:42] Coding Agents and Documentation
[47:40] RocketRide Cloud Overview
[56:27] Wrap up
-
BuzzHPC Roundtable episode: Architecting Modern AI Systems: Platforms, Agents, and Integration
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 BuzzHPC for the collaboration!
// Abstract
As AI systems evolve into more autonomous, agent-driven architectures, the way we design platforms, tools, and infrastructure is rapidly changing. In this session with BuzzHPC, we explore the shifting boundary between platforms and tools, what developers expect platform providers to handle versus what they want to control and build themselves.
We unpack what modern agentic stacks look like today, how teams are structuring them in production, and where these architectures are heading as systems become more complex and distributed. A key focus will also be on agent interoperability, how different agents communicate, coordinate, and operate within shared environments.
Finally, we share insights and lessons from a recent AI hackathon delivered in partnership with Bell, Buzz, Mila, and KHP, highlighting how these concepts are being tested and applied by builders in real-world scenarios.
// Bio
Allen Roush
Allen has held senior technical and AI leadership roles at companies like Oracle and Intel. He's very active in the AI research space and open source communities. He's passionate about improving the creativity and coherence of AI systems.
FrΓ©dΓ©ric BΓ©nard
FrΓ©dΓ©ric is Senior Director of AI Applications Development at Mila (Quebec AI Institute), where he leads a team focused on building the engineering foundations for applied AI systems. His work centers on translating cutting-edge research into scalable applications, including AI-driven platforms and agent-based systems used across research and industry collaborations.
Shuo Wang
Shuo leads the Responsible AI Office for Bell Canada, where all AI use cases are reviewed and assessed for potential harm and bias. Previously, he led a team of data scientists to expand a large-scale ML program to improve customer support effectiveness.
// Related Links
Website: https://www.buzzhpc.ai/
~~~~~~~~ βοΈConnect With Us βοΈ ~~~~~~~
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/]
Connect with Demetrios on LinkedIn: /dpbrinkm
Connect with Allen on LinkedIn: /allen-roush-27721011b/
Connect with FrΓ©dΓ©ric on LinkedIn: /benard/
Connect with Shuo on LinkedIn: /shuow/
-
Big news: the MLOps Community is joining the Linux Foundation to become the official user community of the new Agentic AI Foundation (AAIF).
The AAIF is the neutral home for open source projects like the Model Context Protocol (MCP), goose, and AGENTS.md, co-founded by Anthropic, Block, and OpenAI. With that governance and scaffolding now in place, the open source agent ecosystem has room to scale, and the MLOps Community is right in the middle of it.
Everything you love about the community from the past six years keeps going, and we are adding even more on top.
What this means:
- Official user community: MLOps Community becomes the user community of the Agentic AI Foundation under the Linux Foundation.
- The projects: MCP, goose, and AGENTS.md now live under one open, neutral governance structure built to scale.
- Nothing goes away: The podcast, the global meetups, the weekly newsletter, the Slack workspace, and the virtual events all continue.
- New: Ambassador Program: Just opened for applications, so you can get more involved in the community.
- AgentCon EU: September 17 and 18 in Amsterdam.
- AgentCon North America: October 22 and 23 in San Jose.
- A possible new name: The podcast may become "Agentic Conversations," because honestly all we talk about is agents. Tell me what you think in the comments.
If you build with AI agents or follow the open source agent ecosystem, this is the update to bookmark. This is MLOps Community 2.0.
Links and Resources:
- MLOps Community: https://mlops.community
- MLOps Community 2.0: https://mlops.community/blog/mlops-community-2-0
- Agentic AI Foundation: https://aaif.io
- Ambassadors: https://aaif.io/ambassadors
- Linux Foundation AAIF announcement: https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation
- AgentCon and MCPCon events: https://events.linuxfoundation.org/aaif-events/
- Model Context Protocol (MCP): https://modelcontextprotocol.io
- goose: https://goose-docs.ai
- AGENTS.md: https://agents.md
Timestamps (approximate, adjust before publishing):
00:00 The big announcement
00:12 Joining the Linux Foundation's Agentic AI Foundation
00:30 Why it matters: MCP, goose, and AGENTS.md
00:48 What is not changing: podcast, meetups, newsletter, Slack
01:15 What is new: the Ambassador Program
01:30 AgentCon EU in Amsterdam and North America in San Jose
01:55 A new name for the podcast: Agentic Conversations?
02:10 MLOps Community 2.0
#AgenticAI #MCP #LinuxFoundation
-
Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation) from Just Eat Takeaway.com join the MLOps.community to pull back the curtain on how one of Europe's largest food delivery platforms is running an internal innovation engine. From autonomous delivery robots to agentic AI voice assistants, they share what it actually takes to build like a startup inside a 40,000-person company.
Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce // MLOps Podcast #377 with Just Eat Takeaway.com's Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation)
π€ Delivery Robots β How JET partnered with RIVR and DELIVERS.AI to deploy physical AI ground robots in Zurich, Milton Keynes, and Bristol, and what the first pilots taught the team
π§ AI Incubation at Scale β How Nidhi's team built a dedicated incubation unit to fast-track AI experiments without the red tape of a large enterprise
ποΈ AI Voice Assistant β The story behind JET's new voice-first food ordering experience, and the ML challenges of building a conversational concierge at scale
π¦Ύ Physical AI vs. Software AI β Why deploying wheeled-legged robots in real cities is fundamentally different from shipping a model update, and the MLOps implications
π Corporate Innovation Playbook β The frameworks Guthrie and Nidhi use to move from idea to pilot in weeks, not quarters, inside a large org
π¦ Innovation as a Platform β How JET is thinking about turning its delivery infrastructure and AI capabilities into a reusable platform for new business lines
π Startup Partnerships β What makes a good external innovation partner (vs. building in-house), and how JET evaluates robotics and AI startups for pilots
β‘ Agentic AI & Accessibility β How agentic AI is being used to make food ordering genuinely accessible for blind and low-vision users
Whether you're an ML engineer at a large company trying to get AI into production, a product leader navigating corporate innovation, or a startup founder looking to partner with a platform player β this conversation is packed with practical lessons.
π Links & Resources:
Just Eat Takeaway.com: https://www.justeattakeaway.com
RIVR (physical AI delivery robots): https://www.rivr.ai
DELIVERS.AI (UK delivery robots): https://www.delivers.ai
Prosus (JET parent company): https://www.prosus.com
MLOps.community: https://mlops.community
β±οΈ Timestamps
[00:00] AI Innovation Incubator Strategy
[03:16] Everyday Convenience Expansion
[07:03] Context Ownership in Ecosystems
[17:35] LLM Integration and Discovery
[24:02] Whoop Notifications Grievances
[33:01] Expanding Beyond Food
[48:20] Innovation Lab Failures
[51:22] Rory Sutherland's Alchemy
[1:03:23] Latency and Conversational Design
[1:13:42] Drone Delivery Efficiency
[1:18:06] Wrap up
#AgenticCommerce #VoiceAI #DroneDelivery
- Laat meer zien
