How to Build Your First AI Agent: A Step-by-Step Tutorial

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You’ve read about agentic AI. You’ve seen the demos. Now you want to build one yourself. The good news: building your first AI agent in 2026 is surprisingly accessible. You don’t need a PhD, you don’t need expensive infrastructure, and you can have a working agent in under an hour.

This step-by-step guide takes you from zero to a working AI agent using free tools.

What We’re Building

A research agent that can:

  1. Take a research question from you
  2. Search the web for relevant information
  3. Read and analyze the results
  4. Synthesize findings into a structured report
  5. Cite its sources

This is a practical agent that you’ll actually use — and it demonstrates all four agentic capabilities: planning, tool use, memory, and reflection.

Prerequisites

  • Python 3.9+ installed
  • A Google AI Studio API key (free from ai.google.dev)
  • Basic Python knowledge

Step 1: Install Dependencies

pip install langchain langchain-google-genai tavily-python

We’re using LangChain as the agent framework, Gemini as the AI model, and Tavily for web search.

Step 2: Set Up the Agent

import os
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools.tavily_search import TavilySearchResults
from langchain.prompts import PromptTemplate

os.environ["GOOGLE_API_KEY"] = "your-gemini-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

# Initialize the model
llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0)

# Give the agent a search tool
tools = [TavilySearchResults(max_results=5)]

# Define the agent's behavior
prompt = PromptTemplate.from_template("""You are a research agent. Given a question, search for information, analyze multiple sources, and provide a comprehensive answer with citations.

Question: {input}
{agent_scratchpad}""")

# Create and run the agent
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "What are the latest trends in AI agent development?"})
print(result["output"])

Step 3: Watch It Work

When you run this, you’ll see the agent’s thought process in real-time:

  1. It reads your question and decides it needs to search the web
  2. It formulates a search query and calls the Tavily search tool
  3. It reads the search results and decides if it needs more information
  4. It may search again with a refined query
  5. It synthesizes all the information into a coherent answer

This is agentic behavior — the AI is making decisions about what to do next based on what it’s learned so far.

Step 4: Make It Better

Once your basic agent works, enhance it:

  • Add more tools — file reading, web scraping, calculator, code execution
  • Improve the prompt — add instructions for output format, citation style, depth of analysis
  • Add memory — let the agent remember previous research sessions
  • Add reflection — have the agent evaluate its own output quality before returning

From Tutorial to Hackathon Project

This research agent is a starting point. At hackathons on Reskilll, teams have extended similar agents into:

  • Government scheme finders that match citizens with eligible programs
  • Medical research assistants that analyze symptoms and suggest next steps
  • Legal research agents that find relevant case law and regulations
  • Agricultural advisors that combine weather, soil, and market data

The Agentic India hackathon series saw 2,200+ teams build agents like these — many starting from exactly this kind of simple foundation and building something impressive in 24-48 hours.

The Build With AI bootcamps teach this hands-on, with mentors guiding you through the process. If you prefer learning by doing under pressure, find your next hackathon on Reskilll.

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