MLOps.community

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

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 headingIf 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=rUNjFuNqRvGvAEWwqms7TALinks & 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/naji247Timestamps:[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 — 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🤖 What is Dust? — 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.🧠 From OpenAI Research to Startup Founder — Stanislas's journey from studying mathematical reasoning in LLMs under Ilya Sutskever to co-founding an enterprise AI company in Paris with Gabriel Hubert.🚀 The $40M Series B — 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.🔄 The Outer-Loop Era — 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.⚠️ What Most Enterprise AI Gets Wrong — The biggest mistakes companies make when deploying AI agents, why adoption fails, and how Dust achieves 70%+ weekly adoption rates.📊 Building Reliable Agent Infrastructure — Lessons from scaling to thousands of companies: observability, governance, data security, and why enterprise AI is harder than it looks.🛠️ Horizontal vs. Vertical AI Platforms — 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.🔗 Links & Resources:• Dust: https://dust.tt• Stanislas Polu on X/Twitter: https://x.com/spolu• Dust on LinkedIn: https://www.linkedin.com/company/dust-tt• Dust $40M Series B announcement: https://dust.tt/blog• "The Outer-Loop Era" talk by Stanislas (dotconferences): https://www.youtube.com/watch?v=_outer_loop• Dust + Stripe MCP integration: https://stripe.com/customers/dust• Dust + Datadog observability case study: https://datadoghq.com/case-studies/dust⏱️ 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

Rafael (Head of Innovation, iFood) and Daniel (Data and AI Manager, iFood) pull back the curtain on ILO-Agent — iFood's conversational AI ordering system built for 200 million users across Latin America. Recorded live at AI House Amsterdam, this conversation goes deep into the engineering and product decisions behind building recommendation systems and agentic AI, and why the speed of your AI's response might actually be destroying user trust.The Latency Goldilocks Zone Explained // MLOps Podcast #376 with iFood's Rafael Borger (Head of Innovation) and Daniel Wolbert (Data and AI Manager)🍕 Recommendation Systems at Scale — Why personalizing for 200M users with wildly different food tastes, budgets, and cultures is a fundamentally different problem than standard ML🤖 ILO-Agent Deep Dive — What iFood's conversational AI agent actually does, how it handles open-ended requests ("a romantic dinner for two, my wife hates onions"), and where it's headed⏱️ The Latency Goldilocks Zone — The fascinating insight that LLM responses can be too fast (users don't trust them) or too slow (users abandon) — and how to find the sweet spot🧠 Perceived vs. Actual Latency — Why showing progress indicators and partial results can make a 6-second response feel instant, and how iFood uses this in production🛒 The Tinder for Food Experience — How iFood is experimenting with swipe-based discovery to solve "I don't know what I want to eat" for millions of undecided users🗣️ Voice vs. Text AI Interfaces — Why voice ordering limits you to 6 items in 30 seconds, and why text-based agents need radically different output design🔗 Agent-to-Agent (A2A) Architectures — What happens when your customer support agent and your ordering agent need to collaborate, and the standardization challenges ahead📊 Measuring Product-Market Fit for AI — Why the Sean Ellis / Chanel score method breaks down in Brazil, and what iFood uses instead🏗️ Scalability vs. Ecosystem Health — The real tension between consuming partner APIs aggressively and keeping the food delivery ecosystem sustainable🌎 Building AI for Global-Local Markets — Why one-size-fits-all AI products fail and how iFood builds for cultural and economic diversity simultaneously. This episode is for ML engineers, AI product managers, and data scientists building production AI systems at scale — especially if you're working on recommendation, retrieval, or agentic systems in consumer apps.🔗 Links & ResourcesMLOps.community: https://mlops.communityAI House Amsterdam: https://aihouse.amsterdamiFood: https://www.ifood.com.br/iFood AILO launch coverage: https://tiinside.com.br/en/10/10/2025/ifood-lanca-ailo-assistente-de-ia-que-inaugura-pedidos-por-conversa/iFood AI case study (AWS): https://aws.amazon.com/solutions/case-studies/ifood-bedrock/Related MLOps Community talk — "From Zero to AILO" by Nishikant Dhanuka & Chiara Caratelli: https://home.mlops.community/public/videos/from-zero-to-ailo-lessons-learned-from-building-ifoods-ai-agent-nishikant-dhanuka-and-chiara-caratelli-2025-11-25ZenML LLMOps database write-up on iFood's hyper-personalized agent: https://www.zenml.io/llmops-database/building-a-hyper-personalized-food-ordering-agent-for-e-commerce-at-scale⏱️ Timestamps[00:00] Recommending the unknown[00:18] Ailo Hyperpersonalization Insight[06:24] Predictive Personalization Insights[09:13] "Jet skis" of innovation[17:45] Consumer Behavior and Chatbots[26:33] Perceived Latency and Engagement[33:22] AI-driven UI Evolution[38:17] LCM Voice Mode Inquiry[45:20] Chat as Interface[47:46] Wrap up