Building an AI Coding Agent from Scratch Using Python
- Staff Desk
- 12 minutes ago
- 4 min read
AI coding agents are systems that can read files, understand code, modify files, and perform actions inside a development environment. These agents work by combining large language models with tools that allow them to interact with files and systems.
This blog explains how a basic AI coding agent can be built from scratch using Python, without relying on heavy frameworks. The goal is to understand how agents work internally rather than treating them as black boxes.
What Is an AI Coding Agent?
An AI coding agent is a system that can:
Read files from a directory
Understand file contents
Modify existing files
Create new files
Decide when to take actions instead of only responding with text
Unlike a normal AI chat response, an agent can use tools, take actions, and loop through decisions until a task is complete.
Core Idea Behind an Agent
At its core, an AI agent consists of:
A language model
A set of tools the model can use
A loop that allows the model to:
Think
Act
Observe results
Continue if needed
The model does not directly perform actions. Instead, it decides which tool to use, and the system executes that tool.
Project Structure Overview
The entire agent can live in a single Python file of around 200 lines. For learning purposes, the code can be broken into logical steps:
Basic setup and API access
Agent class definition
Tool definitions
Tool execution logic
Chat loop
Command-line interface
System instructions
Each step builds on the previous one.
Step 1: Basic Setup
The first step is simply making sure the environment works:
Python is installed
Dependencies can be loaded
The AI model can be called
At this stage, the agent does nothing except confirm that it can start successfully.
Step 2: Defining the Agent Class
The agent class holds three main components:
Client: Used to communicate with the language model
Messages: Stores the conversation history
Tools: A list of actions the agent is allowed to take
This structure mirrors how most modern AI systems work internally.
Step 3: Understanding Tools
A tool is a structured description of an action the agent can perform.
Each tool includes:
A name
A description
An input schema
The schema defines what data the tool needs. This allows the model to generate valid inputs when calling the tool.
Essential Tools for a Coding Agent
A minimal coding agent needs only three tools:
1. Read File
Allows the agent to read the contents of a file using a file path.
2. List Files
Allows the agent to view all files and folders in a directory.
3. Edit File
Allows the agent to:
Replace text inside a file
Create a new file if it does not exist
These tools give the agent full visibility and control over the codebase
Step 4: Implementing Tool Logic
Each tool must be backed by real Python functions.
Examples include:
Using open() to read files
Using os.listdir() to list directories
Using string replacement to edit file contents
Error handling is added so the agent can recover instead of crashing.
Step 5: Executing Tools
When the model decides to use a tool, it returns a structured request.
The system then:
Reads the tool name
Extracts the input parameters
Executes the matching Python function
Sends the result back to the model
This creates a feedback loop that allows multi-step reasoning.
Step 6: Adding the Chat Loop
The chat loop allows interaction with the agent.
The flow works as follows:
User input is added to message history
The model is called with messages and tools
The model responds with:
Text, or
A tool call
Tool calls are executed
Results are added back to the context
The model runs again if needed
This loop continues until the agent finishes the task.
Why Tool Loops Matter
Many tasks require more than one step.
For example:
Listing files
Reading a specific file
Editing that file
Confirming changes
The loop allows the agent to break complex tasks into smaller actions.
Step 7: Creating a Command-Line Interface
A simple CLI allows continuous interaction:
The user types commands
The agent responds
The session continues until exit
This mimics real coding assistants and makes testing easier.
Step 8: Adding System Instructions
System instructions define how the agent behaves.
Examples include:
Output format rules
Tone and verbosity
Environment assumptions
Clear instructions improve consistency and reduce formatting errors.
Why This Approach Works
This agent works because:
The tools are limited and well-defined
The context remains manageable
The model is guided instead of overloaded
It demonstrates that powerful agents do not require massive frameworks.
Key Takeaways
AI agents are built around tools and loops
The model decides what to do, but does not act directly
Clear tool definitions are critical
Error handling improves reliability
Simple systems are easier to control than complex ones
Final Thoughts
Building an AI coding agent from scratch helps clarify how modern AI tools work behind the scenes. Understanding this structure makes it easier to debug, improve reliability, and design better AI systems. This approach focuses on clarity, control, and simplicity, which are essential principles when working with AI agents in real-world applications.
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