Agentic Conversations (formally mlops.community)

Two people, one tandem bike, and a lot of near misses along Amsterdam’s canals. Dexter Horthy, CEO and co-founder of HumanLayer and the person who coined “context engineering,” climbs on the back to talk about a problem every AI-heavy engineering team eventually hits: code that runs today that nobody understands three months later.

The ride starts with an old joke from the Claude 3.7 days: ship the slop, it’ll be GPT 7’s problem. That joke is aging badly now that GPT 6 is out and still can’t design clean abstractions. From there it becomes a tour of how agents are actually used right now. Computer use checks in for flights and books government appointments. Two hours of raw footage became a rough but promising product demo through Opus 4.8 and the Palmier MCP while Dex was out for a run. And the best MCP tools turn out to be the ones that mirror what Claude Code and Codex were trained to call.

Off the bike, over pour-over coffee, the questions get bigger. Are browser agents a bandaid until everything goes headless? What happens to ad-funded websites and upsells when an agent does the shopping? Why does chat still feel like such a low-bandwidth way to work with models? And why is every principal engineer quietly building their own software factory, then getting buried in the sync, streaming and approval plumbing that HumanLayer wants to turn into building blocks, the way GitHub Actions did for CI?

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkmDexter Horthy: https://www.linkedin.com/in/dexterihorthy

Timestamps[0:00] Intro[1:38] The GPT 7 will fix it joke[3:03] GPT 6 still writes spaghetti[6:21] If it runs, does it matter[7:28] Computer use that fills out your forms[11:57] Editing a demo with Palmier MCP[13:16] Why most MCP servers are bad[14:32] Mirror the tools models were trained on[16:57] Hill climbing a demo while out running[23:49] Are browser agents a band-aid[32:23] Why chat needs richer interfaces[40:30] Every engineer is building a software factory[45:13] The GitHub Actions moment for agents

A large language model knows everything and nothing at the same time. Tim O’Reilly, founder and CEO of O’Reilly Media, joins us ahead of his keynote at AGNTCon + MCPCon North America to talk about what that paradox means for the people building with these models.

We start with what Tim is bringing to the stage in San Jose, then move to the craft of steering a model. The right words work almost like spells that open up new regions of latent space. Something as small as telling a model to use a computer can unlock a new tier of capability, where agents go off and do real work.

From there, the conversation turns philosophical. Tim makes the case for mechanism design: shaping the rules of a system so the outcomes we want become the natural ones. We connect it to a book, The Score, and the question of which game we are really playing. In Risk, everyone fights over the board. In charades, everyone plays together. We close on Tim’s view that AI should be an ecosystem rather than a product that sucks the air out of the room, so that everyone can build on top of it and flourish.

Demetrios Brinkmann: https://www.linkedin.com/in/dpbrinkm
Tim O’Reilly: https://www.linkedin.com/in/timo3

Timestamps:
[00:00] Cold open
[01:02] Grappling with the bitter lesson
[02:22] The value of human expertise
[04:54] Why AI needs us
[05:37] Culture is what remains
[06:23] The toil audit story
[08:26] AI knows everything and nothing
[08:57] Magic words and runaway agents
[11:18] Brute force vs human context
[12:32] Turning scar tissue into infrastructure
[14:12] Google and the first bitter lesson
[16:25] Silicon Valley's sad turn
[17:43] Artificial volition
[19:08] Casting spells in latent space
[20:46] The peaceful atom lesson
[22:15] Sharing value with creators
[24:54] No jobs means no customers
[25:43] Mechanism design explained
[26:35] Unix and the architecture of participation
[28:45] The missing mechanisms of the AI economy
[29:36] What game are you playing
[30:43] The game of human flourishing
[32:04] Big labs vs open source AI
[33:47] Wrap up

AI can write your code, choose its own tools, and book your next trip. But who checks that it actually did the job?

In this episode of Debug Mode, we explore what it takes to give AI agents more responsibility—and how to keep track of what they’re doing. We discuss intent-based tool selection, MCP and WebMCP, and why a convincing demo doesn’t always translate into reliable automation.

We also get into the personal AI assistant everyone seems to be building: one that manages travel, handles household tasks, and hopefully remembers to order coffee before you run out.

Then it’s back to code: QA agents, automated verification, tests that capture business intent, and the growing challenge of reviewing AI-generated contributions.

Timestamps:[00:00] Who gave AI the keys?[00:43] A tattoo years in the making[02:40] Tell the agent what you want—not which tool[06:19] MCP enters the agent harness[07:23] Great demo. What do the benchmarks say?[09:55] AI builders rediscover machine learning[11:50] Can WebMCP make agents more reliable?[16:04] Unlocking tools like a video game skill tree[18:19] Let an agent try to break your website[19:50] Are humans still in the driver’s seat?[20:38] Build for the outcome, not the API[24:32] Why can’t AI keep my coffee stocked?[27:38] A house manager for the rest of us[29:23] A travel agent that actually handles things[30:49] When your AI chat becomes your workspace[34:12] Does superintelligence need a rebrand?[35:06] Who checks the code AI writes?[36:31] Test why the code exists—not just if it works[41:08] Give your coding agent a QA partner[43:18] The checks failed. Why submit it anyway?[44:33] Open source meets the AI code flood

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?