Building Your Own AI Agent: From Agent Harness to Local Small-Model Fine-Tuning
ORIGINAL / How to Build Your Own AI Agent (Like PewDiePie)
Using PewDiePie's Odysseus as a case study, the video breaks down how an agent harness runs, how tool calling works, how to run models locally with llama.cpp and Hugging Face, and how to fine-tune a small model like Qwen 3.5 9B on agent traces. It is a practical reference for developers who want to understand or build their own agents.
01 ABSTRACT
The video uses PewDiePie's Odysseus to explain the basics of an agent harness and tool calling. It also discusses running models locally with llama.cpp and Hugging Face, and fine-tuning Qwen 3.5 9B on agent traces so the model calls tools more reliably inside a custom harness. The specific training methods and configurations are the presenter's explanations and suggestions, not general conclusions verified by evidence in the video.
02 KEY FINDINGS
- Uses Odysseus as an example to break down an agent harness, tool calling, and context (system prompt, tools, memory).
- Compares local models with cloud APIs regarding privacy, latency, and offline use, and covers finding open-source models on Hugging Face.
- Introduces local inference with llama.cpp, matching models to hardware and quantization (Q4 vs. BF16), and fine-tuning Qwen 3.5 9B on agent traces.
AI GENERATED SUMMARY / DISCOVERED BY HUGGING FACE