How AI Agents Actually Fix Code (Agentic Loop Explained) - Part 1 Theory

Build an autonomous AI agent that finds and fixes bugs in your code without human intervention. This tutorial shows you how to create a Cursor-style bug-fixing agent using local LLMs, function calling, and an agentic loop.

🔧 What You'll Learn:
- How to set up Ollama with local LLMs (qwen2.5:14b)
- Implementing function calling for AI agents
- Building an agentic loop that explores, fixes, and verifies code
- Creating secure file operations with path validation
- Handling structured and text-based tool calls
- Testing your agent on real bug-fixing scenarios

📚 In This Video:
Introduction to Agentic AI
- Project setup with UV package manager
- Building core functions (get_files_info, get_file_content, write_file, run_python_file)
- Implementing the agentic loop
- Live demonstration fixing a temperature converter bug

🛠️ Tech Stack:
- Python 3.13+
- Ollama (local LLM)
- OpenAI-compatible API
- UV package manager
- Function calling / Tool use

💻 Code Repository:
https://github.com/cholakovit/bug-hunter-ai

🌐 Visit my website for more tutorials and projects:
www.cholakovit.com

👍 If you found this video helpful, please like and subscribe for more AI and coding tutorials!

📝 Full code and documentation available in the repository.

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Видео How AI Agents Actually Fix Code (Agentic Loop Explained) - Part 1 Theory канала cholakovit