Something has shifted in the developer community over the past year. AI agents have moved from “interesting research concept” to “thing my team is actually building.” The prototypes are working. The demos are impressive. And now comes the harder question: How do we ship this?
That question turns out to be a multi-part one. Agents don’t behave like traditional software. They reason, act, and adapt, which means they need different approaches to testing, memory, orchestration, and security. The patterns that served us well for deterministic code don’t fully translate.
To help developers work through these challenges, we’ve published a collection of guides covering the full agent lifecycle. These resources first appeared during Kaggle’s 5 days of AI Agents Intensive, and they’ve proven so popular and useful, we wanted to make sure a wider audience had access, as well.
These guides offer practical frameworks and code samples you can adapt to your own projects. Below, we’ll walk through the key concepts — from agent architecture to production deployment — so you can decide where to dig deeper.
