Agents write the code. Who builds the place it runs? Everyone is building software factories: agents triage, implement, verify, and ship. Google reports 75% of new code is AI-generated, and Uber attributes over 70% of pull requests to agents. But every one of those changes still needs somewhere to run, and in most companies that somewhere still arrives through a ticket. Code got cheap. Environments did not. Max Körbächer introduces the Infrastructure Factory: a platform that turns intents from humans or agents into compliant, owned, observable, cost-attributed environments, with no ticket in the path. He looks at why the most-cited software factory talks of 2026 call infrastructure the hardest part but none treat it as a factory of its own, how it relates to the AI factory and where the two share a floor, and what the factory looks like when its front door is an MCP server, and its policy gate is agentgateway. The target: zero tickets.

Talks recorded at Agentic AI Foundation community chapter meetups, starting with AAIF Munich. Practitioners share what they are building with agents, what breaks, and what they learned along the way. Find upcoming Munich meetups at https://luma.com/aaif-munich

Lots of companies say they have AI agents in production. Far fewer seem to have built their own, and almost nobody wants to admit it's really hard. This conversation pulls apart what "agents in production" actually means, with both speakers drawing on their own work: one is hosting a virtual conference for the third time, the other is prepping a livestream for AgentCon and MCPCon in San Jose. Their case: plenty of agents already do real work. They're just Claude Code and Codex, not homegrown platforms, and nobody earns brownie points for saying so.

Sam Herring’s first real test of Hermes Agent was a shopping tool that matched clothes to photos of ancient pottery. A few months later, the same agent was checking government auction sites every day until it found him a retired three-wheel parking ticket car, which he now drives around San Francisco. In this episode, recorded at AGNTCon + MCPCon North America in San Jose, Sam, who works on the RL post-training team at Nous Research, goes from those everyday wins to the question everyone in AI is asking: can agents improve themselves?

What does AI really cost once thousands of people inside a company start using it? Ioannis Zempekakis, Director of AI at Prosus, has been running agents in production since 2024. We put that question to him on a tandem bike ride from a favorite Amsterdam cafe to Vondelpark. Ioannis explains the decision to stop relying purely on cloud APIs, and why that approach breaks down at the scale of a large company. He covers why agent harnesses and sandboxes are as greedy as software gets, and why agents scheduled to run every five minutes repeat a lesson the data world already learned about freshness. He also explains how the habit of throwing away a codebase and prompting again quietly adds up. His bigger point is that even if model progress froze tomorrow, there would still be about two years of value left to squeeze out. The cheapest wins come from controlling context and running a basic model router, while ten new features a month keep sprouting like hydra heads.

Your AI agent can read your inbox, plan your trip, and act through your browser. But when it uses your login, how much access should it really have? In this episode of Debug Mode, we unpack agent identity, permissions, and the challenge of giving AI more responsibility without handing it the keys to everything. Through a fictional finance agent named Ryan, we explore prompt injection, why an agent’s intent matters, and what fraud detection could teach us about safer AI systems. Then we get practical: daily briefings across email and messaging apps, using chat as a control center, planning a trip to Japan, and tackling German paperwork. Along the way, we ask where convenience becomes risk—and when a human should still make the final call.

What does it take to build AI agents that deliver useful, reliable work? Stanford lecturer and Monaco Head of AI Mihail Eric joins Demetrios for a conversation inside a Waymo about how AI is changing software development - and what it takes to put agents into production. Mihail explains why he rebuilt most of his Modern Software Developer course, how he teaches students to avoid shipping AI slop, and why research, planning, and verification still matter as coding agents become more capable.

Humans used to give infrastructure a breather. AI agents don’t. In this episode of Debug Mode, Erika and Demetrios explore what happens when one person’s prompt triggers a burst of searches, API calls, and compute-intensive requests - and why existing services, gateways, and pricing models can struggle to keep up.