Building My First AI Agent

Source: Dev.to

Introduction

AI agents have become increasingly prominent in today’s technology sector, and their momentum shows no signs of slowing. They are now an integral part of the workflow for technology knowledge workers worldwide, achieving this status in just a few months.

I recently followed a tutorial by Thorsten Ball—one of my favorite writers—that provides a roadmap for building your own AI agent. I was drawn to the tutorial for several reasons: expanding my knowledge of these tools, using Go (a language I love but don’t get to use at my day job), and trusting Thorsten’s clear explanations of dense concepts.

Below is a high‑level overview of my experience building a coding agent, along with the key takeaways.

Setting Up the Project

1. Project scaffolding – Created the usual Go project files, including main.go.

2. Anthropic SDK – Added the Anthropic SDK and defined an Agent type with a NewAgent constructor that accepts:

   • an Anthropic client
   • a getUserMessage function

   This type and constructor were wired into main.go.

3. API key – Inserted my Anthropic API key and implemented a run function that takes a context and a conversation slice.

4. First run – After wiring the key and the run function, I could run Claude in the terminal. It greeted me with:

   “Chat with Boris, slug head”

   (a nod to the GoldenEye N64 game).

Creating the Agent Wrapper

Simply wrapping an LLM to run locally is useful on its own, but a functional coding agent needs to perform a few standard actions:

• Read files within a working directory
• List files in that directory
• Edit files within the working directory

These actions share a common pattern:

1. Define a strict schema for precise input and output.
2. Marshal and unmarshal JSON to pack/unpack data.
3. Register the tool’s namespace (read, write, list) in the main executable.

Integrating these three “action pillars” allowed my agent—named GoldenEye—to operate similarly to the agents I use at work and at home.

Implementing File Actions

The tutorial’s section on file actions was the most challenging, but the process boiled down to:

• Schema definition – Explicit JSON structures for each action.
• JSON handling – Consistent marshaling/unmarshaling to ensure correct data flow.
• Namespace registration – Adding each tool to the agent’s toolbox so it can be invoked during a conversation.

Once these were in place, GoldenEye could read, list, and edit files on demand.

Takeaways

• Context is king – Providing clear, explicit prompts (e.g., read_file({path: "main.go"})) dramatically improves the agent’s ability to act as intended.
• Simplicity of wiring – Connecting a model to your code locally is straightforward; the real differentiators are:
  • The quality and relevance of the underlying model.
  • The user experience, including UI design and performance.
• The agent loop is largely solved – Most of the heavy lifting is done; the remaining edge comes from how well you present the tool and manage context.
• Future focus – I’m eager to explore the next abstraction layer: the models that power these agents. Understanding their architecture and capabilities will deepen my grasp of AI intelligence.

References

• How to Build an Agent by Thorsten Ball – the tutorial that inspired this work
• Anthropic API Documentation
• GoldenEye repository – my implementation