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.

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.