Agentic Conversations (formally mlops.community)

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.

His reasoning is what he calls "Sonnet maxxing." A model that reasons less and stops sooner keeps developers involved, and that matters a lot when the codebase is banking software full of hard business rules. The result was 800,000 lines of code, about 60% of them written by Sonnet, shipped by a team that never stopped thinking for itself. Pep also explains how to justify the spend once the token-maxxing era ends and the CFO starts asking questions. He covers a scatter chart that plots token spend against output, the uncomfortable question of whether a 1% better home screen was worth $20K in tokens, why he measures gains in headcount rather than 2x or 3x multipliers, and where he fell short when trying to prove ROI at HelloFresh.

We also get into a codebase test you can run yourself, why AI recommendations quietly narrow your choices, and multiplayer refinement sessions where the whole team works through the agent's questions together. Pep shares an experiment where designers and engineers got the same one-shot prompt, and it showed how words like "scalable" and "maintainable" steer a model. We finish with how his team built a meta harness on top of Claude Code, after finding that 85% of their code followed the same path: refinement, specification, implementation, verification.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm
Pepijn Senders: https://www.linkedin.com/in/pepijn-senders

Timestamps:
[0:00] Can you still read your codebase
[1:15] Meet Pep from HelloFresh
[2:35] What is Sonnet Maxing
[3:41] Rebuilding a platform in six months
[5:02] Why Sonnet made adoption easier than Copilot
[6:37] Proving the ROI of AI coding
[8:50] Fighting for AI ROI at HelloFresh
[11:02] Token spend vs output
[13:24] Tinder pickup lines on the company dime
[14:13] Measuring ROI in headcount saved
[18:26] Keeping humans in control of AI code
[22:26] When Opus and Fable stop speaking English
[24:19] Multiplayer refinement sessions with AI
[28:16] Magic words that steer the model
[32:32] Inside the company Meta harnesses

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.

This episode asks whether “dangerously powerful” AI is a security concern, a marketing pitch, or a little of both. Then we put agents to work: booking trips, tackling German paperwork, drawing diagrams, and navigating the chaos of online shopping. Plus: what happens to checkout, advertising, and customer data when your best customer is a bot?

Chapters:[00:00] Is AI Danger the Ultimate Sales Pitch?[00:43] Powerful Models, Payments, and Big Questions[01:10] Too Dangerous to Release, Too Good to Ignore?[03:19] Why Take the Test When You Can Steal the Answers?[08:36] A Security Failure or a Marketing Win?[11:02] Powerful AI, Forbidden Cat Questions[16:00] AI Only Needs to Find One Unlocked Door[20:52] Welcome to the World Wild Web[22:47] What Happens When You Give AI a Wallet?[26:00] Could ChatGPT Become the Next Amazon?[29:50] Mum Wants a Holiday. Can AI Handle It?[33:38] Can AI Beat German Bureaucracy?[36:28] Claude, Draw My Diagram[39:39] Why Is Connecting Your Email Still So Hard?[44:24] Stop Giving Me Options. Just Buy the Sofa.[46:40] When Bots Shop, Who Owns the Customer?

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.

We get into the split between computer-use and browser-use agents on one side and API and MCP agents on the other. We also watch a real example of the idea in action: an agent pulling speaker photos from Flickr, hunting down company logos, and loading everything into Canva so the human only touches the creative part. From there, Sarah shares the unlikely origin story of WebMCP (and who's to blame for it). We then pressure-test a bold idea: a WebMCP gateway that could cover the huge share of websites that will never ship support on their own.

Along the way, we cover the instinct that separates senior engineers, the ability to sense which business problems are actually worth solving. We also talk about how thought experiments cut through a pile of competing priorities, the hacky WebMCP workarounds already showing up in the wild, and why the best agent experiences are the ones that delight customers in ways they never saw coming.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm
Sarah Drasner: https://www.linkedin.com/in/sarahdrasner

Timestamps:
[0:00] Cold open
[0:52] The AGNTCon keynote
[1:23] Change the web or fix agents
[3:35] Browser agents vs MCP agents
[5:48] An agent that builds thumbnails
[7:03] Keeping the creative work human
[9:12] Agent allowances and engineering instinct
[13:34] Thought experiments and priorities
[15:23] The WebMCP origin story
[17:08] A WebMCP gateway for the web
[18:53] It works, but it is not ideal
[19:44] Hacky WebMCP in the wild

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.

We dig into standards as metadata you pin to an agent, so that it shows up with everything it needs in its harness. Then we test the idea against the obvious objection: if a big model can already handle the task, why scope down at all? The answer covers forcing functions, a microservices mindset for agent design, and small agents that quietly check issues or unread messages every five minutes. It also covers the surprise $1K bill waiting for anyone who leaves their agents running.

Then it turns into a proper debate. Our host is 99% sure the agent marketplace will never happen, and Justin arrives with a counter that might change his mind (hint: think about where your skills come from). Along the way, we cover agents as tools versus a big-brain router and how anyone keeps track of a thousand agents. We also talk about why the people who know what agents can do and the people who know what they need still have to meet in the middle.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm
Justin Shroeder: https://www.linkedin.com/in/jpschroeder

Timestamps:
[0:00] Cold open
[1:03] The future is domain-specific agents
[1:51] God models vs specialist agents
[2:20] The perfect Gmail agent
[3:14] Why standards are missing
[3:29] Train the prompt, not the model
[4:30] The harness beats the model
[6:07] Standards and runtimes for agents
[8:17] Why scope down at all
[10:07] Why model routing won't win
[10:29] The surprise $1K bill
[11:12] Agents that build agents
[12:09] Agents as tools
[13:23] Managing a thousand agents
[14:24] Agents for Main Street
[15:37] Agent marketplaces will never happen
[16:52] Skills are just bad agents
[18:04] Skills are already a marketplace
[20:12] Domain experts build the agents
[22:12] Wrap up

What happens to an MMORPG when half the players never log off? Issac Lee, who runs AI and blockchain experiments at NEXUS, has been testing that idea. His team is filling game worlds with agent players instead of scripted NPCs, so a new title feels alive from day one.

We get into why agent-generated content could be the next step after studio-made and user-generated games. We also cover how agent populations might solve the chicken-and-egg problem every multiplayer launch faces, and what it means when your fellow players are relentless and never need sleep. Isaac walks through the NEXUS lab: a text-to-3D world builder for Roblox, an AI pipeline that turns ONE store novels into short web dramas, a loop-based tool for natural pixel animation, and Da Vinci, the internal multi-agent framework the team uses to ship.

Then the conversation turns to infrastructure. We talk about why NEXUS went from 200 hand-defined agent roles to a much thinner harness, whether custom harnesses still matter now that Claude and Codex exist, and why the Kimi release pushed the company to start building its own small data center for inference. We close on context: how much of your life you should hand to an AI, what personalized meeting notes could look like, and why shopping agents may force every ecommerce site to rethink who it's designing for.
Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkmIssac Lee: https://www.linkedin.com/in/issacley/?locale=ko

Timestamps:[0:00] Cold open[0:57] AI in gaming at NEXUS[2:19] Text to 3D and the last 10 percent[4:38] AI companions and coaches[6:35] Agent players in MMORPGs[8:27] From UGC to agent-generated content[11:07] Agents that never sleep[12:58] Taming fuzzy prompts[19:24] Inside Da Vinci[23:10] Undercover QA agents[27:14] Do harnesses still matter[31:52] Self-hosting open models[41:33] Lab experiments and Bob[50:42] How much context is too much[54:22] Marketing and shopping agents