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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.

MCP, 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.
🔗 MCP & Standardization — Why the Model Context Protocol matters, what's trivial vs. what's transformative about MCP, and how Dust integrates MCP-compatible servers for enterprise workflows.
🚀 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

James Everingham is the CEO and Co-founder of Guild.ai — 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 — and what engineering leaders need to build before it's too late.

🎯 Single-Agent vs. Multi-Agent Systems — 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.
🔍 The Agent Control Plane — 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.
⚠️ Non-Determinism at Scale — Why AI agents behave like employees, not software, and why you need workforce-style governance — not just observability tooling — to manage them.
💸 Token Spend & Cost Visibility — How teams running agents in production are flying blind on cost, and what Guild shows you that your current stack doesn't.
🏗️ Lessons from Meta's DevMate — 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.
🚦 Agent Identity & Governance — Why every agent needs an identity, what happens when they don't have one, and how agent sprawl becomes a governance crisis fast.
🔄 Sharing Agents as Infrastructure — Why Guild treats agents as shared production infrastructure rather than one-off scripts, and how that changes the economics of AI investment.
🛠️ Framework Agnosticism — 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.

🔗 Guild.ai: https://guild.ai
🔗 James on X/Twitter: https://x.com/jevering
🔗 James on LinkedIn: https://www.linkedin.com/in/jameseveringham
🔗 Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/

⏱️ 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

🤖 WHAT WE COVER:
🔹 Control vs. Magic — Thiago's spectrum model for thinking about AI agents, from deterministic pipelines to fully autonomous systems
🔹 iFood Pago Explained — How iFood's embedded fintech arm uses AI agents to provide credit, loans, and financial services to restaurants
🔹 WhatsApp as an AI Interface — Why WhatsApp is the primary channel for merchant interactions in Brazil and how agents are deployed there
🔹 Multi-Agent Architecture — Why single monolithic agents break down and how to split them into sub-graphs with specialized contexts and tool sets
🔹 Context Engineering — Why what you put in the agent's context window is more important than the model itself
🔹 Human-in-the-Loop Design — How to build trust with merchants while minimizing friction in agentic workflows
🔹 LangGraph in Production — How Thiago's team uses LangGraph to build stateful, multi-agent pipelines
🔹 Debugging with AI — Generating on-the-fly HTML/JavaScript visualization tools to investigate data pipeline problems
🔹 The Cost of Software Going to Zero — What happens to demand when software becomes nearly free to build
🔹 Personalization at Scale — Serving millions of restaurants with AI that knows their business context

🎯 This episode is for AI engineers, ML practitioners, and fintech builders who want to understand what production agentic AI looks like beyond the demos.

🔗 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

⏱️ 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 — 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

🔑 What's covered:
🪵 Logs vs. The Three Pillars — 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 — and dramatically simpler to instrument.
🚨 AI-Generated Alerts, Not AI-Evaluated Alerts — Instead of using AI to triage your noisy alert stream, Sazabi generates the alerts autonomously from your logs and codebase — so you never configure a monitor again.
🤖 Agent Sandboxing & Bash Access — 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.
🧠 Agentic Memory via Git — Sazabi's novel approach to persisting agent memory across threads using Git branches — enabling multiple parallel sub-agents to share findings without bloating the context window.
🔀 Multi-Agent Parallelization — 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.
📊 Why Evals Are Hard (and What They Built Instead) — 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.
⚡ MCP Servers, Skills Bloat & Context Management — 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.
🎯 Building a Moat in 2026 — 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.
🚀 Beta Launch & Who It's For — 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.
👉 Perfect for: AI engineers, SREs, DevOps teams, and founders building production-grade agent systems who are questioning whether their current observability stack is overbuilt.

🔗 Links & Resources
🌐 Sazabi: https://sazabi.com
📄 Sazabi on Y Combinator: https://www.ycombinator.com/companies/sazabi
💼 Sherwood Callaway on LinkedIn: https://www.linkedin.com/in/sherwood-callaway
📰 SiliconANGLE coverage: https://siliconangle.com/2026/04/08/startup-sazabi-bets-on-logs-and-ai-agents-to-replace-traditional-observability-stacks/
💻 MLOps.community: https://mlops.community

⏱️ 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 — 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  ⁨RocketRide⁩  for this collaboration!

🔑 What's covered:
🏗️ Why AI infrastructure needs standardization — how coding agents produce inconsistent "glue code" across projects and why a typed node graph fixes it
⚡ Efficiency AI vs. Opportunity AI — the two paths companies take with generative AI, and which one actually compounds growth
🔀 Multi-agent pipeline orchestration — running CrewAI, LangChain, and DeepAgent side-by-side to benchmark which works best for your use case
💰 Cutting LLM costs in half — design-time strategies for routing tasks to cheaper models without sacrificing output quality
🔍 Pipeline observability & debugging — logging every node step in dev and production so you can pinpoint exactly where a 10-step pipeline breaks
🖼️ Beyond text: image, video & audio nodes — frame grabbing, OCR, Whisper transcription, and speech-to-text running on shared GPU infrastructure
🚀 RocketRide Cloud — one-click deploy from local to cloud with dynamic GPU scaling and cost-efficient shared inference
🧠 Intentionality in agentic development — why moving fast with AI agents creates "crappy code fast" and how skills/context files change the equation
🔌 MCP support & framework-agnostic design — 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.
🔗 Links & Resources:
• RocketRide website: https://rocketride.ai
• RocketRide open source (GitHub): https://github.com/rocketride-org/rocketride-server
• AIDE VS Code Extension: https://rocketride.org
• MLOps Community: https://mlops.community
• Discord: https://discord.gg/Hd4PukFt2H

⏱️ 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