How to Build a Coding Agent from Scratch: A Practical Guide for Developers

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AI-powered coding agents are rapidly transforming how software is written, debugged, and deployed. While products like GitHub Copilot and Cursor offer a glimpse into the future, many developers are curious about the inner workings. How do these agents actually operate? What architecture enables them to generate, test, and iterate on code?

This guide breaks down how to build a coding agent from scratch using large language models (LLMs), function calling, retrieval-augmented generation (RAG), code execution and LLM workflows. The goal is to provide developers with the conceptual foundations and code-level insights needed to build and adapt their own agent pipelines.

Why Build Coding Agents?

Coding agents represent a new class of AI tools capable of automating multi-step software development tasks. Rather than serving as static code completion tools, modern agents can:

• Read, write, and modify files
• Identify and retrieve relevant code context
• Run and debug scripts
• Iterate on open-ended tasks like data analysis or refactoring

Used effectively, agents become collaborators capable of reducing boilerplate, speeding up experimentation, and improving developer productivity.

Step 1: Enable Function Calling

LLMs are fundamentally limited by their context window and inability to access real-time or external data. Function calling addresses this by giving the model the ability to request that specific tools or functions be executed on its behalf.

For example, if the model is prompted with:

"Read the contents of secret.txt"

It cannot know the contents unless that file is explicitly included in the prompt. With function calling, the model can return a structured request like:

    
      {
        "function": "read_file",
        "args": {
            "path": "secret.txt"
        }
      }
    

Your application handles the actual execution, reads the file, and returns the output to the LLM. The model then incorporates this new information into its reasoning.

Core Tools for a Coding Agent

At a minimum, a functional coding agent should support:

• list_files(): to enumerate project files
• read_file(path): to read file contents
• edit_file(path, old, new): to modify files

These operations form the backbone of an agent that can navigate and manipulate a local codebase. For an implementation of this in code see the guide here.

Step 2: Handle Retrieval and Context

Injecting the entire codebase into an LLM's prompt quickly hits context limits. Instead, effective agents retrieve only the code that is relevant to the current task.

This retrieval process can be implemented in several ways:

• Manual hints: Let the user highlight code or specify filenames
• Text-based search: Use keyword matching (e.g., grep)
• Semantic search: Embed code snippets into vector space and retrieve using similarity scoring

Embedding models allow you to encode functions, classes, and files into numerical vectors where similar code has shorter distances. For example, embeddings of bubble sort and heap sort are closer than embeddings of a sorting algorithm and a Snake game written in Pygame.

By comparing user prompts (or code) against these embeddings, agents can identify and load only the most relevant code context before responding. For an in code implementation of retrieval using a code embedding model see the code notebook here.

Step 3: Add Code Execution Capabilities

To move beyond static analysis, agents need to run the code they generate. This enables several critical workflows:

• Test execution and verification
• Runtime error detection and correction
• Profiling and performance tuning
• Exploratory data analysis

The Together Code Interpreter provides a safe, sandboxed environment where the agent can execute Python code, capture output, and iterate on its solution. You can configure the agent to loop through a cycle of:

1. Generating code
2. Executing it
3. Receiving output or error messages
4. Updating the code
5. Repeating until success or termination condition

This loop forms the foundation of agents that can solve real-world tasks without human intervention. TCI also support file uploads and can return generated images back to the code agent. For a detailed look at TCI capabilities see the code notebook here.

Step 4: Choose the Right Workflow Architecture

Agents can be orchestrated in various workflows depending on the use case:

• Sequential workflows: Linear step-by-step reasoning (e.g., plan -> code -> test -> deploy)
• Parallel workflows: Multiple agents solving the same task independently and aggregating results
• Conditional workflows: Agents making decisions based on branching logic or evaluation
• Iterative workflows: Agents refining outputs based on prior feedback or execution results

Each architecture serves different needs, from reliability (ensemble voting) to speed (parallel execution) or adaptability (iteration).

Real-World Example: A Data Science Agent

Together AI released a data science agent that demonstrates these capabilities in practice. You can give it a dataset and a prompt like "Explore this CSV and visualize feature distributions."

The agent will:

• Load the data into memory
• Plan an EDA (exploratory data analysis) workflow
• Generate code to analyze and visualize the data
• Execute each code block in sequence
• Display intermediate plots and summaries
• Iterate until the task is complete

This implementation uses a variant of the ReAct framework, combining reflection, planning, and tool use in a loop.

Explore the open-source notebook to try it yourself!

Getting Started

You can experiment with each component using these step-by-step notebooks:

• Function Calling and File I/O
• Code Retrieval with Embedding Models
• Executing Code with Together Code Interpreter
• Agent Workflows
• Open Data Science Agent
• Together AI Cookbooks

Conclusion

Building a coding agent from scratch is now within reach for any developer familiar with Python and LLM APIs. By combining tool use, context-aware retrieval, and runtime execution, these agents can go far beyond text generation to act as real assistants in the software development process.

For more resources, join the Together AI developer community or explore documentation at docs.together.ai.