Build Your Own AI Research Agent in Under 10 Minutes
Instead of paying hefty monthly fees for popular AI services, you can build your own AI agent for free. In this article, I'm going to show you how to build a custom AI agent that can browse the internet, perform work for you, and generate detailed research reports, all completely autonomously. It's like having your own AI employee.
Why pay for deep research when you can build an AI agent that does it for you? The tool we're about to build allows you to specify exactly what you want your AI agent to do, and it will go onto the internet to get the job done. You can assign it a mission, and it will accomplish it online. This is a service people pay hundreds of dollars for, and you can build it for free.
When you give it a mission, the agent breaks it down into multiple steps. When you command it to execute, the AI agent connects to the internet and begins working on each step to build a comprehensive research report. It follows the step-by-step plan, executing tasks, doing research, and essentially working for you autonomously. If you follow along, by the end of this article, you will have built your own AI agent that performs tasks on the internet.
What Makes an AI Agent?
Before we start building, let's discuss what makes an AI agent an agent. There are five key components we will build into our AI agent that distinguish it from a simple AI chatbot. This is the difference between a basic AI chat and your own AI employee that actively performs tasks.
These five components are:
- Planning: When you provide a goal, it creates a step-by-step plan to achieve it.
- Tool Use: It has a set of tools it can use to execute its plan.
- Autonomy: It executes the plan autonomously, so you can step back while it works.
- Reflection: As it progresses, it reviews its actions, reflects, and can refine its plan.
- Goal-Oriented: Its actions are driven by the primary goal you assign it.
These five characteristics are typically absent in standard AI chatbots. This is what we're going to build right now to create our own AI employee. Don't worry if some of this seems complex; we're going to make it incredibly simple. You won't need to write any code or have any technical expertise. We're using AI to build the entire AI agent.
Setting Up the Foundation
To begin, we need to provide an initial, powerful prompt to an AI-powered code editor. Using an advanced AI model to generate this first prompt ensures it is detailed and gives us a strong starting point.
Here is the kind of high-level prompt you can use:
"I want to build a research AI agent using a popular web framework. I need to be able to give it a mission, which it will complete by breaking it down into steps. It should have access to web search via a search tool to gather information. Please provide the initial prompt I can use in an AI code editor to start building this agent."
This initial prompt outlines our objective, and the AI model will generate a detailed starting script for our code editor, ensuring a solid foundation for our project.
Building the Application Step-by-Step
For this project, we'll use an AI-powered code editor with a generous free tier. This AI will be our developer, making it straightforward to build the application.
Inside the editor, an AI composer will build the application for us without requiring any manual coding. We'll use the "agent" mode, which allows the AI to write and execute code. While you can use various models, a powerful and cost-effective one will yield excellent results.
After pasting the initial prompt generated earlier, the AI will start building the application. It begins by creating a clean project using a popular web application framework, which is simple and easy for the AI agent to work with.
The editor will suggest commands to install the project. You simply approve them, and the editor runs the commands, writes the code, and handles the technical details. It builds the application, installing all the necessary technologies. Once the project is created, the AI navigates into the project directory and sets up the required files and folders, a process that would typically take hours but is done in seconds.
After running the development server with a simple command like npm rundev, you can view the basic application in your browser at localhost:3001 or a similar address. Congratulations, you've just built your first web app!
Crafting the User Interface
What's great about using a detailed initial prompt is that the AI editor already knows the next steps. We don't need to guide it manually. We can simply instruct it to proceed to the next phase: building the user input interface.
The AI will then write the code for the UI. Once completed, you'll see a clean interface for the "Research AI Assistant." It will have a field to enter a mission and even provide example missions. Although the "Start Research" button won't be functional yet, the UI structure is in place. You'll also see a confirmation screen for when a mission is received.
Integrating AI Logic: The Planning Phase
Now, let's review our progress on the core components of an AI agent: - Goal-Oriented: Done. We can give the agent a goal. - Planning: Next up. We'll build the planning capability.
For the next step, we'll integrate an AI API. This allows us to send the mission to a powerful AI model, which will then return a step-by-step plan for our agent to execute.
The AI editor will handle installing the necessary API client and creating an environment variable file for your API key.
Getting Your API Key
To get an API key, you'll need to sign up on the platform of a major AI provider. Once registered, you can navigate to the API keys section and create a new secret key. Copy this key and paste it into your .env.local file in your project.
With the API integrated, the application can now send a mission to the AI model and receive a plan in return. The AI editor also helpfully sets up a README file, describing how the application works, which is useful for future collaboration.
Let's test it. If we input a mission like, "What are the major differences between Claude 4 and Claude 3.7?" the application should send this to the AI API and get back a research plan. The result is a beautifully generated plan with an overview and detailed research steps.
However, the initial plan might include steps the agent can't perform, like conducting user surveys. We need to refine this. We can instruct the AI editor with a simple prompt: "One small change: make sure the AI is aware it only has access to web search as a tool." The editor will update the underlying prompt to ensure the generated plans are aligned with the agent's capabilities.
After this change, a new plan will be generated with steps relevant only to web search, such as searching for articles, analyzing blog posts, and finding public reports.
Adding Web Search Capabilities
We've handled goal-orientation and planning. Now, let's give our agent tools. All AI agents need tool-use capabilities. We'll implement web search so our agent can browse the web.
For this, we'll use a dedicated web search tool designed for AI agents, which also offers a generous free tier. We'll ask the AI editor to build the integration.
Getting the Search API Key
Similar to the AI API, you'll need to sign up for the web search service (e.g., Tavily). Once you have the API key, add it to your .env.local file.
The AI editor will build the integration, update the folder structure, and even update the README file. It's important to test each component as you build. A new "Test Search" button will appear in the UI. Let's test it with a query like "electric vehicle market analysis." The search tool is incredibly fast, returning an AI-generated summary and multiple live sources from the internet in mere seconds.
Automating the Research Process
With the web search tool working, let's connect it to the planning component. This is where we implement autonomy and reflection.
- Goal-Oriented: Done.
- Planning: Done.
- Tool Use: Done.
- Autonomous & Reflective: Next up.
We'll use the following prompt in the AI editor: "This is looking good. Let's connect the web search to the plan so that when we enter a mission, the agent makes the plan and then executes on it using the search tool."
The AI will then write the code for the automatic plan execution. It builds the component that takes the plan and executes each step autonomously. The AI is essentially building an agent that creates its own research plan and then carries it out automatically.
The Final Result: Your Autonomous AI Agent
Once the AI finishes, the autonomous research agent is complete. Let's test the full workflow.
- Mission: "What is the difference between Claude 4 and Claude 3.7?"
- Start Research: The agent processes the mission and generates a multi-step plan.
- Execute Plan: The agent autonomously goes through each step, using the web search tool to gather information. It compiles all findings into a final report.
Initially, the report might just be a summary of each step. We can improve this with one final prompt: "I'm not in love with the final research report. I want to take all our sources and information, send it to the AI API, and get back a complete, synthesized report."
The AI editor will build the components to generate a more complex, in-depth final report. After it's done, let's run the agent one last time.
The final research report is now incredibly detailed: - Executive Summary: A high-level overview of the topic. - Key Findings: A breakdown of the most important points (e.g., over 6 key findings). - Detailed Analysis: A full breakdown of the comparison. - Conclusion: A summary of the analysis. - Recommendations: Actionable advice based on the findings.
This entire, in-depth report, based on numerous sources, was generated in just a few seconds. This is the power of AI agents. You now have an AI employee that can conduct research for you, boosting your productivity immensely.
You can continue to evolve this agent by adding more tools, such as the ability to control your desktop. With an AI-powered code editor, if you can think of it, you can build it. You've just created a powerful tool that many companies charge hundreds of dollars for, and you did it for free in just a few minutes.
