Google and Kaggle opened enrollment for a free 5-day AI agents course

Google and Kaggle are launching a free five-day online course on AI agents again, with registration for the new cohort opening on April 27, 2026. The intensive itself will run from June 15 to 19. This is a return of the format that the companies have already tested at the end of last year: the first AI Agents Intensive Course then attracted more than 1.5 million listeners, according to Google. Now the program is being relaunched with updated content, new speakers, and a mandatory practical final project in the form of a capstone project, so that participants do not limit themselves to watching materials, but develop their own idea to a working result.

The course is designed for five days and is conducted entirely online. Google describes it as an intensive that takes listeners from basic principles to production-ready agent systems—that is, solutions that can not only be built for demonstration but also prepared for more serious application. Registration is free, which means the barrier to entry is deliberately kept minimal: you just need to sign up and allocate time to complete the program.

For Google and Kaggle, this is also a way to transform the topic of AI agents from a trendy discussion into a mass practical skill for developers, analysts, product managers, and everyone already working with generative AI.

The main theme of the intensive is so-called vibe coding. In the course description, Google uses this term for an approach in which natural language becomes the primary programming interface: the user formulates the task in text, clarifies constraints, checks the result, and step by step assembles the needed logic.

An important point is that the course promises to cover not just quick demo scenarios, but full-fledged workflows where an agent can plan actions, invoke tools, connect to external APIs, and work as part of a larger system. This transition from a single prompt to a sustainable process becomes the main educational goal of the program.

Each day, according to the announcement, will combine conceptual breakdowns and practical examples. This is an important detail because the market for AI courses is saturated with either general lectures or scattered tutorials without architectural context. Here, emphasis is placed on the integration of theory and practice: participants are promised to learn how to create more efficient agent systems through connecting tools and APIs, and then consolidate everything in a final project.

It is separately emphasized that the program has been updated compared to the November launch, so you can expect fresher use cases and examples, closer to the real tasks of teams that are already implementing an agentic approach in their products and internal processes.

The relaunch has a broader context. Over the past months, AI agents have finally moved beyond the status of a trendy term and have become a separate engineering discipline: companies are testing autonomous and semi-autonomous scenarios, developers are building assistants on top of LLMs, and product teams are seeking ways to not just generate text but automate actions.

Against this backdrop, Google benefits from not only promoting its own tools but also training the audience in practical patterns for working with agents. Kaggle, for its part, provides a clear educational framework and a familiar platform for mass online learning, where interest in the topic can quickly be turned into real practice.

What this means in practice: the market will quickly normalize agentic development as a fundamental skill rather than an experiment for enthusiasts.

For participants, this is a convenient entry point—the free format, short duration, clear dates, and emphasis on practice reduce the risk of getting stuck in abstract theory. If the updated program truly offers more applied scenarios and a strong final project, the course will be useful not only to beginners but also to those already experimenting with LLM applications and wanting to move from single prompts to systems with tools, APIs, and clear task execution logic.