AI agents explained: Build your first agent in 8 minutes

AI agents explained: Build your first agent in 8 minutes

Follow the codelab → https://goo.gle/3Q5TSt3

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)

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_outline key (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_post key (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).

AI agents are autonomous systems that leverage Large Language Models (LLMs) to reason, plan, and execute multi-step tasks independently. Unlike basic chatbots that just answer questions, an agent accesses tools (like search engines, databases, or APIs) to complete workflows, self-correct its mistakes, and reach specific goals without human intervention. [1, 2, 3]

How to Build Your First AI Agent in 8 Minutes

You can build a fully functional, autonomous AI agent in about 8 minutes using no-code platforms or lightweight Python frameworks. Here is a step-by-step approach utilizing beginner-friendly workflows: [1, 2, 3]

1. Define the Goal
Choose a specific, repetitive task you want to automate, such as compiling daily tech news into social media scripts or generating weekly reports. [1, 2]

2. Select the Platform

No-Code Options: Use workflow builders like n8n to visually connect triggers (e.g., a Telegram message or a daily schedule) to AI logic and external tools. [1, 2]

Code Options: Use frameworks like Google’s Agent Development Kit (ADK), CrewAI, or LangChain to set up multi-agentic workflows (e.g., a “planner” agent and a “writer” agent) in a Python environment. [1, 2]

3. Connect Your Tools
An agent is only as powerful as the tools it can use. Configure API integrations like Google Calendar, Gmail, or web search so your agent can retrieve real-time data and take action autonomously. [1, 2, 3]

4. Set the Logic & Loop
Program the agent to follow the ReAct (Reasoning and Acting) Framework. Give it instructions to: [1]

Observe: Understand the prompt.

Think: Create a plan of action.

Act: Use connected tools to execute steps.

Validate & Loop: Check for errors and retry if necessary. [1, 2, 3]

5. Test and Deploy
Run the agent in a test or “plan mode” to review its intended actions before letting it run autonomously on a set schedule. [1, 2]