First, Smitha breaks down the theory behind modern agents: the ReAct Framework (reasoning and acting) and the 3 main agent patterns (sequential, reactive, and planning). Then, she jumps straight into Python to build a practical Blog Writing Agent*. Watch along and learn how to combine *Planner and Writer agents with validation checkers and loop agents to create an AI that catches its own mistakes and automatically retries until it gets it right.
Chapters:
00:00 – AI Agents Explained
01:05 – The ReAct Framework Explained
02:15 – The 3 Types of AI Agents (Sequential, Reactive, Planning)
03:30 – Project Overview: The Auto-Correcting Blog Writer
04:15 – Setting Up Google ADK & UV
04:50 – Coding the Planner Agent
05:40 – Adding Auto-Correction (Validation Checkers & Loop Agents)
06:50 – Coding the Blog Writer & Root Agent
In the context of the Google Agent Development Kit (ADK) demonstrated in the video, agents share information primarily through a shared state.
When an agent performs a task, it saves its output to a specific key within this shared state. Subsequent agents in the workflow are then configured to retrieve information from that same key.
Key examples from the project include:
- Blog Outline: When the blog planner agent finishes, it stores its markdown outline under the
blog_outlinekey (4:08). The blog writer agent is then able to access this data to generate the full post (5:16). - Blog Post: Once the blog writer completes its draft, it saves the content under the
blog_postkey (5:42), which allows further processing or validation steps to occur.
This architecture ensures that information flows predictably between specialized sub-agents, such as the planner, writer, and validation checkers, allowing for complex multi-agent workflows (6:38–7:07).
