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 happens when you keep a bear in a cage built for a bunny? That’s our analogy for AI agent security: the agents aren’t the problem, the cages are. OAuth was designed to stop people handing their passwords to third parties, not to fence in agents. In this catch-up episode of Debug Mode, one of us is just back from Japan and from AgentCon and MCPCon EU in Amsterdam. We get into why loading more skills can make an agent worse, and what is actually new about Jev, the fast classifier API, when classifiers have been around for years. Think support bots that bring in a human before the customer loses patience, or a math tutor that can quickly tell whether a seven-year-old is still asking about math. Then we take on a question from Amsterdam: is computer use just a stopgap until everything goes headless? Which service do you think will never ship an MCP server? Tell us in the comments, and subscribe for the next episode.