Why (Senior) Engineers Struggle to Build AI Agents — Philipp Schmid, Google DeepMind

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>> Okay, cool. Awesome. Hi everyone. My name is Philip. I work at DeepMind everything related to agents on Gemini or Gemini API. So if you have some questions afterwards, some concerns, some bugs, some issue, please let me know. We're going to talk today 10 minutes about why engineers struggle to build agents and I see this every day

internally at Google but also externally at Google and I brought five example on like what's really different to how we built traditional software a few years ago and to now how we build agents. And if we like on a high level compare them, right? When we wrote software, we created a spec, a PRD,

wrote code, sometimes created tests to make sure our code works. We deployed it and then our user used it. And when building agents, things are a little bit different. We define instructions on what we want our agent to do. We run it, we observe what it does, we maybe adjust our prompts, maybe we adjust our tools.

We run it again and we have like this iterative loop of how can we improve and make our agent way more reliable, which is very different to how we build software. And like something I like to compare it to is like traditional software is more like we acted as a traffic controller, right? We had

control over the street lights or how fast you can go, which roads you can use, basically how the car drives. And now with agents, we are more of a dispatcher. We tell the agent, "Hey, I want to go to London and I'm from like Germany. I could use the train, I could fly, I could use my car and go like