If you are a developer or reader interested in moving beyond simple LLM prompts and building genuinely autonomous, complex AI systems, I strongly recommend the 5-Day AI Agents Intensive Course with Google. This comprehensive program, originally held live from November 10–14, 2025, was crafted by Google’s ML researchers and engineers and is now available for free as a self-paced guide on Kaggle and YouTube.

This course is much more than a conceptual overview — it’s a hands-on path designed to help developers explore the foundations and practical applications of AI agents. By the end, you’ll be ready to build, evaluate, and deploy agents that solve real-world problems.

A Complete Framework for Advanced AI

What makes this intensive guide so valuable is its comprehensive focus on the full architecture required for production-ready systems, moving beyond simple LLM prototypes. The curriculum centers on the five core components of advanced AI agents: models, tools, orchestration, memory, and evaluation.

Building the Foundation and Enabling Action

The course starts by laying the groundwork for intelligent, autonomous systems. Day 1: Introduction to Agents explores the foundational concepts, defining characteristics, and how agentic architectures differ from traditional LLM applications. You immediately get hands-on experience building your first AI agent and a multi-agent system using the Agent Development Kit (ADK), powered by Gemini, giving it the ability to use Google Search for up-to-date information.

To make an agent truly useful, it must be able to “take action.” Day 2: Agent Tools & Interoperability dives into how agents leverage external functions and APIs. You learn about the Model Context Protocol (MCP), which simplifies the discovery and use of tools, and the codelabs show you how to create custom actions by turning your own Python functions into tools your agent can perform.

Creating State and Ensuring Reliability

For an agent to handle complex, multi-turn tasks, it needs a memory. Day 3: Context Engineering focuses on building stateful agents. You learn about Sessions — the short-term container for immediate conversation history — and Memory — the long-term persistence mechanism. The hands-on exercises teach you how to manage conversation history and implement long-term memory that persists across sessions, allowing your agent to remember context and have coherent interactions.

Building robust systems requires mastery of quality control. Day 4: Agent Quality focuses on the critical disciplines of evaluating and improving agents. This session provides the necessary technical foundation of Observability, built on three pillars: Logs, Traces, and Metrics, allowing you to gain full visibility into the agent’s decision-making process for effective debugging. You also learn about scalable evaluation methods, such as LLM-as-a-Judge and Human-in-the-Loop (HITL) evaluation, which are essential for optimizing performance.

Deployment and Multi-Agent Collaboration

The final step is transitioning your prototype into a scalable solution. Day 5: Prototype to Production covers best practices for deployment and scaling your agents for real-world use. A key learning point is the Agent2Agent (A2A) Protocol, which enables multiple specialized agents to communicate and collaborate, allowing you to create a truly complex multi-agent system.

High-Quality Resources and Required Setup

In addition to the practical codelabs and hands-on examples that make up the core curriculum, each unit includes valuable resources to enhance your understanding. You have access to summary podcast episodes and dedicated whitepapers for each day’s topic. Optional recorded YouTube livestreams also feature the codelab authors and special guests from Google, providing rich insights into the assignments.

Kaggle Account: Sign up for a Kaggle account and make sure it is phone-verified, as this is necessary for the codelabs.

AI Studio Account: Sign up for an AI Studio account to ensure you can generate an API key.

If your goal is to transition from using pre-built LLMs to designing and deploying sophisticated, reliable AI agents, this intensive course provides the necessary skills and framework to make that jump.