How to Build AI Agents: A Practical Guide for New Developers
We continue our series of articles about AI agents. In the previous article, I shared the customization methods I personally use for GitHub Copilot. In this article, I'll explore the possibilities of creating AI agents and why this field opens new opportunities for developers.
Why AI Agents Are a Window of Opportunity
AI agents are a new direction in development where the market of specialists is still small, but interest from companies is growing. For beginners, this is a chance to enter IT and find their niche, as demand for agent developers and AI solutions is constantly increasing.
How AI Agents Complement Classic Applications
Currently, agents don't completely replace backend services but add a new level of user interaction. For example, if previously a user manually searched for needed information through filters and websites, now an agent can analyze data itself and provide a relevant answer.
Example: Classic Google search, where the user searches for what they need themselves, versus Perplexity AI, where the agent analyzes websites and immediately provides a final answer.
What You Need to Know to Get Started
To create agents, basic knowledge of a programming language is sufficient — most often Python or JavaScript — and fundamentals of object-oriented programming. There are many frameworks and ready-made components that allow you to quickly create agents.
The complexity lies not so much in writing code, but in creating a truly useful agent that solves user tasks.
Example: Agent for Finding Direct Flights
An agent is essentially a class that is given a name, instructions, and necessary tools for interaction. For example, you can create an agent that uses the google_search tool to search for a direct flight between two specified cities.
The user enters their request, and the agent automatically forms a search query, analyzes the results, and provides the most relevant option for a direct flight, responding in text form. Such an agent saves the user time and simplifies the process of searching for airline tickets.
Example Agent Code Using ADK with Gemini
Below is a code example showing how to create a Flight Search Agent using Google's Agent Development Kit (ADK):
from google.adk.agents import Agent
from google.adk.tools import google_search
agent = Agent(
name="flight_search_agent",
model="gemini-2.0-flash",
description="Flight search agent for finding direct flights between cities",
instruction="""
Detailed instructions here
""",
tools=[google_search],
)You can get a demo project with a ready-made agent in this GitHub repository and test its operation yourself.
Multi-Agent Systems: Examples
You can create more complex systems where agents work in parallel or sequentially. For example, when writing an article:
The first agent gathers ideas
The second selects a title
The third writes the text
The fourth checks facts and adds references
As a result, you get higher quality material thanks to the collaborative work of agents.
What Other Frameworks to Use
Besides ADK (Agent Development Kit), there are other popular tools:
- •LangChain — A framework for developing applications powered by language models
- •Llama — Meta's open-source large language model
- •And many others that allow creating agents for different tasks and quickly prototyping solutions
Key Takeaways
Creating agents is a new direction that is changing how humans interact with software. I recommend everyone looking for new opportunities in programming to study and practice agent technologies.
The future is already here — you can create your own agents and take user experience to a new level.
This article was originally published in Russian on The Tech.