Introduction
The 5‑Day AI Agents Intensive with Google and Kaggle proved to be an exceptionally rewarding experience, combining clear explanations with practical, hands‑on work each day. The program moved step by step from basic ideas to real multi‑agent systems, so the learning curve felt exciting rather than overwhelming.
Day 1 – Agents and Architectures
Day 1 introduced the foundations of AI agents and agentic architectures, explaining how agents plan, act, and operate with a degree of autonomy. The contrast between traditional chat-style systems and structured, decision‑making agents made the overall vision of “agentic AI” feel very powerful and modern.
Day 2 – Tools and MCP
Day 2 focused on tools and the Model Context Protocol (MCP), showing how agents extend their abilities by calling external functions and services in a standard way. Seeing how ordinary Python code or APIs could be wrapped as tools made the idea of building practical agent systems feel very achievable.
Day 3 – Memory and Context
Day 3 explored short‑term context and long‑term memory, along with strategies for organizing information so agents can handle long, complex tasks. The emphasis on structured memory and context engineering made it clear that good “remembering” is just as important as good reasoning.
Day 4 – Quality and Evaluation
Day 4 was dedicated to evaluation, logging, and metrics, including patterns like using an LLM as a judge to review outputs. This focus on careful measurement and feedback turned quality from a vague idea into a concrete, repeatable process for improving agents.
Day 5 – Multi‑Agent Systems and A2A
Day 5 brought everything together by covering deployment, multi‑agent systems, and the Agent2Agent (A2A) protocol for structured communication between agents. Learning how agents can advertise capabilities, share tasks, and collaborate through a common protocol made the whole ecosystem feel like a coordinated “team” rather than isolated models.
Capstone Experience
The capstone project asked learners to apply orchestration, tools, memory, evaluation, and multi‑agent coordination in one integrated build. Completing this final piece made the entire course feel not only very enjoyable but also deeply practical, with skills that clearly transfer to real‑world AI projects.
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