Show Notes
This episode is brought to you by the MLflow team. Check out more information at MLflow.org. Mihail Eric is Head of AI at Monaco and Adjunct Lecturer at Stanford University, where he teaches CS146S: "The Modern Software Developer" — the first course in the world dedicated to how AI is transforming every stage of the software development lifecycle. With 12+ years building production AI systems at Amazon Alexa, Storia AI (YC S24), and early-stage startups, Mihail has one of the most grounded, practitioner-level takes on what it actually means to be a software engineer in 2026. The Modern Software Engineer // MLOps Podcast #370 with Mihail Eric, Head of AI at Monaco 🧠 What the modern software engineer actually looks like — why the job description has fundamentally shifted from writing code to designing systems and directing agents ⚙️ Agents require more thinking, not less — why the engineers getting the most out of coding agents are the ones who invest the most upfront in architecture, planning, and codebase structure 🎓 Inside Stanford's "Modern Software Developer" course — what Mihail teaches in the first CS course in the world focused entirely on AI-transformed software development 🏗️ From writing code to designing systems — how the best developers are repositioning themselves as architects of agentic workflows rather than line-by-line coders 🔁 The Build System: how to run agents at scale — practical lessons from building multi-agent pipelines, parallel subagent batches, and automated retrospectives📉 What junior engineers should actually focus on — the skills that remain irreplaceable and the paths that still produce strong software engineers in an AI-first world 🚀 Building Monaco's AI-native revenue engine — what it's like building AI infrastructure for a fast-moving $35M-funded startup disrupting enterprise CRM 🎯 How to ace AI engineering interviews — Mihail's framework for demonstrating real AI engineering competence beyond prompt engineering basics. Essential watching for software engineers, ML practitioners, and engineering managers who want an honest, practitioner-level view of where the profession is going — from someone who's both teaching it at Stanford and building it in production.
🔗 Links & Resources Mihail Eric on LinkedIn: https://www.linkedin.com/in/mihaileric/ Mihail's website: https://www.mihaileric.com Stanford course "The Modern Software Developer": https://themodernsoftware.dev/ Maven course — AI Software Development: From First Prompt to Production Code: https://maven.com/the-modern-software-developer/ai-course Free AI Engineer interview prep course: https://course.aiengineermastery.com/ Monaco (AI-native revenue engine): https://monaco.com MLOps.community Slack: https://go.mlops.community/slack
⏱️ Timestamps 00:00 Intro — Mihail Eric & Monaco 04:00 What has actually changed for software engineers in 2026 09:00 Inside Stanford's "Modern Software Developer" course 15:00 Why agents require more human thinking, not less 21:00 From writing code to designing systems — the architect mindset 27:00 The Build System: running agents at scale in production 33:00 What junior engineers should focus on right now 39:00 Building AI infrastructure at Monaco 44:00 How to demonstrate real AI engineering competence 49:00 Skills that will remain irreplaceable 52:00 Rapid fire/closing thoughts
More from Agentic Conversations (formally mlops.community)
AI can write your code, choose its own tools, and book your next trip. But who checks that it actually did the job?
What happens when you give 200 engineers who have barely touched AI a coding assistant and a six-month deadline to rebuild their platform? Pepijn Senders, a principal engineer who previously led AI enablement at HelloFresh, has done exactly that, and he chose Sonnet over Opus on purpose.
An AI slips out of its sandbox. A chatbot won’t explain your cat’s stripes. And your next shopping spree might not involve you clicking anything.
What happens to the web when the ones browsing it are no longer people? Sarah Drasner, Area Tech Lead at Google, sits down with us ahead of her Agentic Web keynote at AGNTCon + MCPCon North America. She works through that question from both directions: rebuild the web so agents can use it properly, or build agents smart enough to handle the web as it already exists.
Everyone is racing to build one agent that can do everything. Justin Schroeder, co-founder of Standard Agents, thinks the future looks more like thousands of tiny ones. Ahead of his talk on domain-specific agents at AGNTCon + MCPCon North America, he explains why tightly scoped agents might be the smarter bet, even if it feels like a step back toward the single-purpose machine learning models of a few years ago.