This is a semi-structured Study Group designed to help us complete a real-world capstone project by working through the 5-Day AI Agents Intensive Course offered by Google.

We’ll use the course curriculum and resources. Each participant is encouraged to learn at their own pace and apply the material to a project of personal interest.

Duration: 2 Weeks: Nov 7 - 20, 2025

Pre-requisite: Register in advance for the intensive course via: https://rsvp.withgoogle.com/events/google-ai-agents-intensive_2025

Day 1: Introduction to Agents & Agentic Architectures Explore the foundational concepts of AI agents, their defining characteristics, and how agentic architectures differ from traditional LLM applications, laying the groundwork for building intelligent, autonomous systems.

Day 2: Agent Tools & Interoperability with MCP Dive into the world of tools, understanding how AI agents can "take action" by leveraging external functionalities and APIs, and explore the ease of discovering and using tools offered by the Model Context Protocol (MCP).

Day 3: Context Engineering: Sessions, Memory Management Explore how to build AI agents that can remember past interactions and maintain context. Learn how to implement short-term and long-term memory to create more robust agents capable of handling complex, multi-turn tasks.

Day 4: Agent Quality: Observability, Logging, Tracing, Evaluation, Metrics Learn to build robust and reliable AI agents by mastering the critical disciplines of evaluating and improving agents. This session will cover observability, logging, and tracing to provide visibility, alongside key metrics and evaluation strategies to optimize agent performance.

Day 5: Prototype to Production Go beyond local testing and learn to deploy and scale AI agents for real-world use. This session will cover the best practices for deploying your agents so that others can use them, including how to create a truly multi-agent system with the Agent2Agent (A2A) Protocol.

Note: The virtual meeting link will be sent to registered participants closer to the event date.