Google and Kaggle are bringing back their free five-day AI Agents Intensive course from June 15–19, 2026. The updated programme focuses on building AI agents with “vibe coding” workflows, where natural language becomes the main interface for creating software and connecting tools.
The announcement matters because it shows how quickly agent-building is moving from niche developer experimentation into mainstream training. Google says the first version of the course reached more than 1.5 million learners. This return run adds updated content, new speakers and a hands-on capstone project.
The course is really about practical agents
The framing is playful, but the content is not just prompt-writing. Google’s announcement says the course will cover foundational agent concepts, production-ready systems, tool and API integration, and examples that help learners build “10x agents”.
That is the important bit. The agent hype cycle is full of demos where a model clicks around a browser or drafts a document. A useful agent needs more than that: access to tools, clear workflows, guardrails, feedback loops and enough structure that it does the same job reliably more than once.
The course page on Kaggle positions the programme around builders learning what works in AI through practical projects, benchmarks and competitions. That fits Kaggle’s role well: it is less about passive watching and more about getting people to build, test and compare.
Why Google wants builders trained this way
Google has a clear incentive here. If more developers learn to build agents around Google’s AI stack, the company gets a larger ecosystem of people who understand Gemini, Kaggle, cloud tooling and agent workflows.
But there is a broader industry shift too. AI products are becoming less like single chat windows and more like systems: a model, a set of tools, some memory, permissions, task routing and a way to evaluate whether the output was actually useful.
That makes education a competitive layer. The companies that teach developers how to build agents may also influence which platforms those developers choose when they move from experiments to production.
Vibe coding needs discipline, not just speed
“Vibe coding” has become shorthand for building software by describing what you want and letting AI generate much of the implementation. It can be fast, but speed is not the same as quality.
A structured course is a good sign if it pushes learners beyond copy-pasting prompts. The useful version of vibe coding is iterative: describe the goal, inspect the result, test it, connect the right APIs, add constraints and keep tightening the system until it behaves predictably.
That is especially true for agents. A small app can be fixed after it breaks. An agent that acts across files, APIs or customer data needs much tighter boundaries.
What to watch next
The capstone project will be the part to pay attention to. If learners come away with real deployed agents rather than toy notebooks, this kind of course could become a practical on-ramp for teams trying to upskill without waiting for formal AI engineering programmes to catch up.
For Wezebo readers, the takeaway is simple: agent development is becoming a normal software skill. The tools are getting easier, but the advantage will go to people who can turn natural language prototypes into systems that are reliable, measurable and safe enough to use at work.
