building-your-first-ai-agent

Building Your First AI Agent

In the previous posts, we learned how to use ChatGPT, create Projects, work with Codex, and understand the concept of AI memory.

Now we are ready to take the next important step:

Building our first AI Agent.

In this article, we will not start by writing a large amount of code. Instead, we will first understand the architecture of an AI Agent and build a simple Agent conceptually.

Understanding the architecture is important because an AI Agent is more than just an LLM or chatbot.

An AI Agent combines several components that work together to achieve a goal.

What Are We Going to Build?

For our first example, imagine that we want to build a simple Software Development Assistant Agent.

The Agent should be able to:

• Understand a user’s request
• Decide what needs to be done
• Use an LLM to reason about the task
• Remember relevant information
• Use tools when necessary
• Perform actions
• Return the result to the user

For example, the user could ask:

Analyze this C# project and find possible problems.

The Agent should not simply answer with general programming advice.

Instead, it should be able to:

1. Understand the request.
2. Inspect the project.
3. Identify relevant files.
4. Analyze the code.
5. Decide whether additional tools are required.
6. Run tests if appropriate.
7. Analyze the results.
8. Provide a final response.

This is the basic idea behind an AI Agent.

The Basic AI Agent Architecture

A simplified AI Agent can be represented like this:

                         User
                           |
                           v
                    +-------------+
                    |  AI Agent   |
                    +-------------+
                           |
          +----------------+----------------+
          |                |                |
          v                v                v
        LLM             Memory            Tools
          |                |                |
          +----------------+----------------+
                           |
                           v
                       Planning
                           |
                           v
                       Actions
                           |
                           v
                         Result

This diagram is simplified, but it shows the main idea.

The Agent receives a goal from the user and combines an LLM, memory, planning, and tools to accomplish the task.

1. The User

Everything starts with the user.

The user provides a request or goal.

For example:

Find all customers who have not placed an order during the last six months.

Or:

Analyze my application and identify possible performance problems.

Or:

Find information about Kubernetes deployments and prepare a report.

The Agent receives this request and needs to determine what should happen next.

2. The LLM

The Large Language Model (LLM) is the reasoning and language component of the Agent.

Examples of LLMs include models from OpenAI, Anthropic, Google, and other providers.

The LLM can:

• Understand natural language
• Analyze information
• Generate text
• Reason about a problem
• Decide what information may be required
• Select an appropriate tool

However, an LLM by itself is not necessarily an AI Agent.

An LLM is one component of an Agent.

A useful way to think about it is:

LLM = Brain

AI Agent = Brain + Memory + Tools + Actions + Control

This is a simplified analogy, but it helps explain the difference.

3. Instructions

An Agent needs instructions that define its role and behavior.

For example:

You are a software development Agent. Analyze software projects, identify problems, and provide secure and maintainable solutions. Do not modify production systems without explicit authorization.

Instructions can define:

• The Agent’s role
• Its objectives
• Its limitations
• Its behavior
• Security rules
• Available actions

Good instructions are an important part of Agent design.

4. Memory

As we learned in the previous article, memory allows an Agent to maintain useful information.

For example, the Agent may remember:

The user prefers C# and .NET.

Or:

The customer previously reported a payment problem.

Memory can be:

• Short-term
• Long-term
• User-specific
• Application-specific
• Conversation-based

The memory system should be designed according to the requirements of the Agent.

5. Tools

Tools are one of the most important differences between a simple chatbot and an AI Agent.

A chatbot may be able to tell you:

The weather in Stockholm is cold today.

An Agent with access to a weather tool could actually retrieve current weather information.

Similarly, a software-development Agent could have tools such as:

• File system
• Terminal
• Git
• Database
• Web search
• API
• Testing system

For example:

AI Agent
   |
   +-- File System
   |
   +-- Database
   |
   +-- Web Search
   |
   +-- Git
   |
   +-- Terminal
   |
   +-- External APIs

Tools allow the Agent to interact with the outside world.

6. Planning

A complex task usually requires several steps.

Suppose the user asks:

Find the cause of the error, fix it, and verify the solution.

The Agent may need to plan:

1. Find the relevant code.
2. Analyze the error.
3. Identify the cause.
4. Modify the code.
5. Run tests.
6. Analyze test results.
7. Report the result.

Planning is what allows an Agent to move from a high-level goal to a sequence of actions.

The LLM can help determine what steps are necessary.

7. Actions

After planning, the Agent can perform actions through its available tools.

For example:

Read file

Search database

Call API

Run test

Create file

Modify code

Send message

The Agent should only have access to actions that are necessary for its purpose.

This is important for security.

8. The Agent Loop

Many AI Agents operate using a repeated cycle.

A simplified Agent loop looks like this:

             User Goal
                 |
                 v
             Understand
                 |
                 v
               Plan
                 |
                 v
            Choose Tool
                 |
                 v
            Execute Tool
                 |
                 v
          Observe Result
                 |
                 v
          Need More Work?
             /       \
           Yes        No
            |          |
            +-----> Plan
                       |
                       v
                    Result

The Agent can perform several iterations until it has enough information to complete the task.

This is one of the fundamental concepts behind Agentic AI.

A Simple Example

Let’s create a very simple Agent.

Our goal is:

Help a user find information about a software product.

The Agent has:

LLM

To understand the user’s question.

Memory

To remember useful user preferences.

Web Search Tool

To find current information.

Instructions

To provide accurate answers and identify sources.

Now the user asks:

What is the latest version of this software?

The Agent might perform the following process:

Step 1 – Understand

The Agent determines what software the user means.

Step 2 – Plan

The Agent decides that current information is required.

Step 3 – Use Tool

The Agent calls the web-search tool.

Step 4 – Analyze

The LLM analyzes the search results.

Step 5 – Respond

The Agent provides the answer to the user.

The important point is that the Agent did not simply generate an answer from its existing knowledge.

It used a tool to obtain information and then used the information to produce a result.

Our First Practical AI Agent

Now let’s design a small Agent that we could eventually implement in software.

Let’s call it:

Software Developer Agent

Its purpose is:

Help developers analyze and improve software projects.

Agent Instructions

We might define its instructions as:

You are a software development Agent. Help developers analyze code, identify problems, suggest improvements, and run tests when authorized. Prefer secure, maintainable solutions. Ask for confirmation before performing destructive operations.

Agent Tools

We could give it access to:

• File system
• Code search
• Terminal
• Git
• Test runner

Agent Memory

The Agent could remember:

• Developer preferences
• Programming language
• Project information
• Previous decisions

Agent Goal

The user might ask:

Find and fix the bug causing the application to crash during login.

The Agent could then:

User Request
     |
     v
Understand Problem
     |
     v
Search Project
     |
     v
Analyze Authentication Code
     |
     v
Identify Possible Cause
     |
     v
Propose Fix
     |
     v
User Approval
     |
     v
Modify Code
     |
     v
Run Tests
     |
     v
Analyze Results
     |
     v
Report Result

This is a much more powerful workflow than simply asking a chatbot:

How can I fix a login error?

Human Approval

An important part of Agent design is human control.

We should not automatically allow an Agent to perform every action.

For example, reading a file might be relatively low risk.

Deleting a database might be extremely high risk.

Therefore, the Agent should have different levels of permissions.

For example:

Low Risk

• Read files
• Search documentation
• Analyze code

Medium Risk

• Modify files
• Create files
• Run tests

High Risk

• Delete data
• Deploy applications
• Send emails
• Modify production systems

High-risk actions should normally require explicit authorization.

AI Agent vs Chatbot

Now we can see the difference more clearly.

Chatbot

User
 |
 v
Chatbot
 |
 v
Answer

Its primary purpose is conversation.

AI Assistant

User
 |
 v
AI Assistant
 |
 +-- Conversation
 +-- Information
 +-- Task assistance
 |
 v
Result

An Assistant can help the user accomplish tasks.

AI Agent

User
 |
 v
AI Agent
 |
 +-- LLM
 +-- Memory
 +-- Planning
 +-- Tools
 +-- Actions
 +-- Control
 |
 v
Goal Achieved

The Agent can work through multiple steps toward a goal.

Does an AI Agent Need All These Components?

Not necessarily.

There is no single architecture that every AI Agent must use.

A simple Agent might only have:

LLM + Tool

A more advanced Agent might have:

LLM + Memory + Planning + Multiple Tools + RAG + Security + Monitoring

The architecture should be designed according to the problem we are trying to solve.

This is an important principle:

Do not build a complicated Agent when a simple solution is enough.

What Technology Can We Use?

AI Agents can be built using many different technologies.

For example:

Programming Languages

• Python
• C#
• JavaScript / TypeScript
• Java
• Go

LLM Providers

• OpenAI
• Anthropic
• Google
• Other providers

Agent Frameworks

• OpenAI Agents SDK
• Microsoft Agent Framework
• LangGraph
• Semantic Kernel
• Other frameworks

Supporting Technologies

• Databases
• Vector databases
• APIs
• RAG systems
• MCP
• Message queues
• Cloud services

We will explore these technologies in later articles.

Our Learning Path

We have now reached an important point in our AI Agents Step-by-Step series.

We started with:

ChatGPT

↓

ChatGPT Desktop

↓

ChatGPT Interface

↓

Projects

↓

Codex

↓

Memory

↓

AI Agent Architecture

Now we can begin studying the individual technologies that make an AI Agent possible.

We will explore concepts such as:

• LLMs
• Prompting
• Tool calling
• RAG
• MCP
• Agent frameworks
• Planning
• Reasoning
• Multi-Agent systems
• Security
• Monitoring
• Deployment

What Have We Learned?

In this article, we built the conceptual design of our first AI Agent.

We learned that an Agent can combine:

• Instructions
• LLM
• Memory
• Planning
• Tools
• Actions
• Human approval

We also learned that an AI Agent is not simply a chatbot.

The key difference is the ability to work toward a goal using multiple steps, tools, and actions.

Conclusion

Building an AI Agent does not begin with writing hundreds of lines of code.

It begins with understanding the problem and designing the Agent architecture.

We first need to determine:

What is the Agent’s goal?

Then:

What information does it need?

What tools does it need?

What actions can it perform?

What should it remember?

What decisions can it make?

Which actions require human approval?

Once these questions are answered, we can select the appropriate LLM, programming language, tools, frameworks, databases, and other technologies.

This is the foundation of building reliable AI Agents.

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