Building an AI Agent From Scratch

Goal

Build a small AI agent from scratch that can take a narrow task, reason through it in steps, and use a few tools without turning the whole system into a complicated framework.

Why this matters

I do not want to learn agents as a vague category. I want to understand the moving parts by building a minimal version myself:

• prompt structure
• tool boundaries
• memory choices
• failure modes

If I can build a constrained version, I can make better decisions later when the system gets more ambitious.

Hypothesis

My starting hypothesis was that most early agent failures would not come from the model itself. They would come from poor task framing, loose tool contracts, and unclear stopping conditions.

Setup

• One local project folder
• A small set of tools: file read, file write, and command execution
• Structured prompts for role, constraints, and completion criteria
• Markdown notes for logging observations after each run

Steps

1. Defined a single narrow task for the agent instead of trying to make it general-purpose.
2. Wrote down what the agent is allowed to do and what success looks like.
3. Added only a few tools so I could observe behavior clearly.
4. Logged where the agent hesitated, looped, or misread the task.
5. Tightened prompts and tool descriptions after each run.

What worked

• Narrow tasks reduced drift immediately.
• Explicit completion rules helped the agent stop at the right point.
• Short tool descriptions were easier to debug than dense, clever instructions.
• Logging runs after each attempt made the system easier to improve.

What failed

• The first prompt was too broad, so the agent spent effort narrating instead of acting.
• I underestimated how often the agent would choose a reasonable but unnecessary step.
• Some tool names sounded clearer to me than they did to the model.

Lessons learned

The interesting part is not whether the agent can do something once. The useful part is whether I can explain why it succeeded or failed. That pushed me toward smaller experiments, fewer abstractions, and more explicit constraints.

Next steps

• Add one more tool and watch how behavior changes.
• Compare short prompts versus heavily structured prompts.
• Test when the agent should ask for clarification instead of guessing.