Building My First Practical AI Agent
A hands-on journey creating an agent that actually helps with daily coding tasks.
After months of theorizing about AI agents, I finally built one that I use every day. Here's what I learned.
The Goal
I wanted an agent that could help with a common workflow: investigating bugs in a codebase. The agent should be able to:
- Search for relevant files
- Read and understand code
- Suggest fixes with context
- Explain its reasoning
The Architecture
┌─────────────────┐
│ User Query │
└────────┬────────┘
│
▼
┌─────────────────┐
│ LLM (Planner) │◄──────────────┐
└────────┬────────┘ │
│ │
▼ │
┌─────────────────┐ │
│ Tool Selection │ │
└────────┬────────┘ │
│ │
▼ │
┌─────────────────┐ │
│ Execute Tool │───────────────┘
│ (search/read) │ (results)
└─────────────────┘
Key Lessons
1. Start Simple
My first version had too many tools. The agent got confused about which to use. I stripped it down to just three: search_files, read_file, and explain.
2. Context Windows Matter
The biggest practical constraint is how much context the model can hold. I had to implement smart truncation—showing relevant snippets rather than entire files.
3. Let the Model Think
Adding a "thinking" step where the model explains its plan before acting dramatically improved results. It's like rubber duck debugging for the AI.
4. Fail Gracefully
The agent will make mistakes. The key is making those mistakes recoverable. I added confirmation steps for any action that modifies files.
Results
After a few weeks of iteration, the agent successfully helps me with:
- Finding where specific functionality is implemented
- Understanding unfamiliar codebases
- Identifying potential causes for bugs
It's not perfect, but it saves me real time.
What's Next
I'm working on adding:
- Memory across sessions
- The ability to run tests and see results
- Better handling of large codebases
Will share updates as I go.