Comprehensive Guide to Google's AI Agent Learning Resources
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Google recently concluded its intensive 5-day Kaggle course on AI Agents, providing a treasure trove of resources for developers and program managers. Here’s a structured breakdown of the key concepts, whitepapers, and code samples to accelerate your learning journey in building autonomous AI agents.
Introduction to Agents
The first day focused on AI agent fundamentals, exploring the core architecture (perceive, plan, act) and differentiating agents from standard LLMs. The goal is to build systems capable of autonomous action.
Perceive, Plan, Act
Core autonomous agent architecture.
Agent Tools & Interoperability
This session detailed how agents interact with external systems via tools and APIs. It covered the Multi-agent ConverSational Protocol (MCP) architecture, best practices for tool design, and implementing human-in-the-loop approval workflows for critical tasks.
Tools & APIs
Enabling interaction with external systems.
Context Engineering: Sessions & Memory
Discover how agents maintain context. This day differentiated between "Sessions" for immediate conversational state and "Memory" for long-term, persistent learning across multiple interactions, a key for creating truly intelligent agents.
Sessions & Memory
Maintaining context and enabling long-term learning.
Agent Quality & Observability
A critical session for production readiness, this covered the foundations of observability: Logs, Traces, and Metrics. It also introduced advanced evaluation frameworks like LLM-as-a-Judge and Human-in-the-Loop (HITL) feedback for ensuring reliability.
Logs, Traces, Metrics
The three pillars of agent observability.
From Prototype to Production
The final day focused on the agent deployment lifecycle and strategies for scaling. It introduced the Agent2Agent Protocol for system-wide coordination and detailed production readiness on Google's Vertex AI platform.
Deploy & Scale
Moving agents from concept to production.