Google's 5-Day Vibe Coding Course Returns to Kaggle in June

On April 27, 2026, Google and Kaggle announced the return of the 5-Day AI Agents Intensive Course, scheduled to run from June 15 to June 19, 2026. The free online program focuses on agentic development and transitioning from traditional syntax to natural language interfaces. You will learn how to design, build, and deploy production-ready “10x agents” using Gemini 3 and Google Antigravity.

The curriculum covers the entire lifecycle of agent development, from foundational concepts to enterprise scaling. This guide breaks down the core technical modules, the required tooling, and how to structure your environment for the capstone project.

Course Format and Delivery

The 2026 iteration follows the structure of the November 2025 program but introduces updated models and a stronger emphasis on deployment. The daily format requires participants to engage with multiple content streams to complete the daily assignments.

• Codelabs and Assignments: Practical exercises focusing on specific agentic patterns.
• Livestreams and AMAs: Daily broadcasts on Kaggle’s YouTube channel featuring Google engineers and researchers.
• Whitepapers and Podcasts: Supplemental architectural reading and companion audio.
• Community Engineering: Dedicated channels on the Kaggle Discord server for debugging and expert Q&A.

Prepare your local environment by ensuring you have access to a Kaggle account, a Google Cloud project with billing enabled for API access, and the necessary API keys for Gemini 3.

Core Technical Curriculum

The five-day intensive is divided into specialized modules. Each day focuses on a distinct layer of the agent architecture.

Vibe Coding Workflows

The initial phase of the course establishes the fundamentals of using natural language as a primary programming interface. Instead of writing imperative logic for every edge case, you will learn to structure vibe coding environments where the model generates, tests, and refines code based on high-level constraints. The course leverages Gemini 3 Pro, which recently scored 1487 Elo on the WebDev Arena leaderboard, making it highly capable of autonomous code generation and refactoring.

You will configure system prompts that define strict boundaries for the agent’s behavior while allowing it the flexibility to solve complex routing and logic problems natively.

Agent Architecture and Memory

Building an agent that functions over a single turn is trivial; building one that maintains context over a week requires explicit architectural decisions. This module covers engineering “skilled agents” with robust state management.

You will implement mechanisms for long-term memory and context retention across disparate sessions. This involves designing vector stores for semantic retrieval and structured databases for explicit state tracking. By separating the execution layer from the memory layer, your agents can pause, resume, and hand off tasks without losing the thread of execution.

Tool and API Integration

Agents are strictly constrained by the interfaces they can access. You will learn methods for connecting agents to external systems, databases, and APIs. The curriculum covers the creation of specific tools that the model can invoke autonomously to gather data or execute actions.

Furthermore, this section introduces multi-agent communication. You will design orchestration layers where specialized agents pass structured payloads to one another, distributing complex workloads across a network of narrow, focused instances. Understanding how AI agents work in a collaborative swarm is critical for scaling enterprise applications.

Enterprise Scaling and Governance

A local Python script running an agent is not a production system. The enterprise scaling module teaches you how to transition local experiments into scalable, governed fleets.

You will explore observability patterns, learning how to trace agent execution paths, monitor API consumption, and log decision-making trees. Governance models will be introduced to ensure that agents operating at scale adhere to compliance requirements and access controls, preventing unauthorized data exfiltration or unapproved state changes.

Evaluation and Security

The final conceptual module addresses the hardest problem in agentic development: measuring success and ensuring safety. Traditional unit tests struggle with stochastic outputs. You will learn how to evaluate and test AI agents using specialized benchmarking frameworks that assess agent quality against expected behavioral distributions.

Security focuses heavily on prompt injection resistance. You will implement defense-in-depth strategies, isolating the agent’s execution environment and sanitizing all untrusted inputs to prevent adversarial manipulation of the agent’s core instructions.

The Capstone Project and Google Antigravity

The course culminates in an optional real-world capstone project. Participants are tasked with designing, building, and deploying their own custom agent system.

This project serves as the primary introduction to Google Antigravity, Google’s new agentic development platform. Antigravity provides the infrastructure required to host, scale, and monitor the agents you design. You will wire your Gemini 3 Pro instances into the Antigravity control plane, configuring the necessary webhooks, authentication protocols, and memory persistence layers.

Certification and Incentives

Successfully completing the capstone project and deploying a functional agent system earns you an official Kaggle certificate and a digital badge verifying your competence in agentic development. Top project winners receive exclusive Kaggle merchandise and are featured across Google’s social media channels.

Preparation Steps

To maximize your time during the intensive, set up your development environment before June 15. Create your Kaggle account and join the official Discord server to establish connections with the community. Review the documentation for the Gemini API and familiarize yourself with standard JSON schema definitions, as structuring reliable outputs is a prerequisite for connecting tools and building predictable agent workflows.