AI Agents Explained: ReAct Framework, Behavioral Types, and Google ADK
Generated: 2026-06-13 · API: Gemini 2.5 Flash · Modes: Summary
AI Agents Explained: ReAct Framework, Behavioral Types, and Google ADK
Clip title: AI agents explained: Build your first agent in 8 minutes Author / channel: Google Cloud Tech URL: https://www.youtube.com/watch?v=Zqno_vux6d8
Summary
This video provides a comprehensive explanation of AI agents, distinguishing them from traditional chatbots by their ability to reason, act, and adapt. It introduces the foundational concept of “ReAct,” which involves a continuous cycle where an agent reasons (internal thinking), acts (calling tools or APIs), observes (data results), and then adjusts or decides its next steps. This iterative process allows modern AI agents to perform complex tasks by breaking them down into manageable actions, evaluating outcomes, and refining their approach, thereby exhibiting a higher degree of autonomy and intelligence.
The video further categorizes AI agents into three behavioral patterns based on their decision-making and planning capabilities. Firstly, Sequential Agents follow a rigid, step-by-step process, similar to an assembly line, ideal for predictable workflows. Secondly, Reactive Agents make decisions in the moment, offering flexibility for dynamic situations but lacking foresight. Lastly, Deliberative or Planning Agents pause to formulate a multi-step plan before execution, making them suitable for complex goals with dependencies, such as booking travel arrangements. The choice of agent pattern depends on the specific problem’s nature and required flexibility.
To demonstrate these concepts, the video walks through building a sophisticated blog-writing AI agent using Google’s Agent Development Kit (ADK). This multi-agent architecture comprises a Conductor (Root Agent), which takes a user’s topic and delegates tasks, along with specialized Loop Agents for planning and writing. The Planner (Loop Agent) generates a structured blog outline, while the Writer (Loop Agent) drafts the full technical blog post. Each of these sub-agents is paired with a validation checker that assesses its output against predefined criteria, ensuring quality and triggering automatic retries if validation fails.
The blog-writing agent’s design emphasizes robustness and control. The “Robust Blog Planner” and “Robust Blog Writer” are implemented as Loop Agents, allowing them to iteratively refine their outputs based on feedback from their respective validation checkers, up to three times. This built-in self-correction mechanism enhances the reliability of the generated outline and final blog post. Finally, a “Blogger” (Root Agent) orchestrates these planner and writer tools in a clear, controlled workflow, taking a topic, planning, writing, checking, and ultimately delivering a polished blog post with alternate titles and hooks. The video concludes by showing how to run this multi-agent system using the adk web command, providing a practical interface to interact with and observe the agent’s complex reasoning and action steps.
Video Description & Links
Description
Follow the codelab → https://goo.gle/3Q5TSt3 GitHub repo → https://goo.gle/4fsahT8 Google Agent Development Kit (ADK) → https://goo.gle/3Q3enqf
At the simplest level, an AI agent doesn’t just answer—it decides and takes action. In this video, Smitha goes beyond basic chatbots and demonstrates how to build a fully autonomous, self-correcting multi-agent system from scratch using Google’s Google Agent Development Kit (ADK).
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 08:20 - Testing the AI in the ADK Web UI 09:40 - What’s Next? (Connecting to MCP Servers)
More resources: ReAct Paper → https://goo.gle/4oa1oQ9
🔗 Connect with Smitha online: YouTube → https://goo.gle/Smitha-on-YouTube Linkedin → https://goo.gle/Smitha-on-LinkedIn X → https://goo.gle/Smitha-on-X
AIAgents GoogleADK PythonTutorial SoftwareEngineering MachineLearning LLMs
Watch more Modern AI Agents: From Theory to Production → https://goo.gle/Learn-with-Smitha 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Speaker: Smitha Kolan Products Mentioned: Agent Development Kit, Gemini
Tags
AIAgents, GoogleADK, PythonTutorial, SoftwareEngineering, MachineLearning, LLMs