📑 Google Explains AI Agents

Google has a 42-page textbook that is essentially a manual on the architecture of modern LLM applications.

What's interesting inside:

1️⃣ Anatomy of an Agent: A clear separation into Model, Tools, and Orchestration. Google explains (with diagrams) how an agent differs from a simple API call to a model. Spoiler: the presence of a perception-action loop.

2️⃣ Reasoning Frameworks: If you don't understand the difference between ReAct, Chain-of-Thought (CoT), and Tree-of-Thoughts, it's explained here.

3️⃣ Extensions vs Functions: A useful part for engineers.
— Extensions: The agent itself calls the API (Agent-side execution).
— Functions: The agent tells you what parameters are needed, and the code runs on the client (Client-side execution).

4️⃣ RAG and Data Stores: How to integrate vector databases and not mess up the context.

Naturally, all examples are in Python. They show integration with both native Vertex AI tools and LangChain / LangGraph.

🔗 Get it here 🫲🏻

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