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. --- #AI #MachineLearning #Python #Ollama #FunctionCalling #AgenticAI #AIAgent #BugFixing #CodingTutorial #PythonTutorial #LLM #LocalLLM #Qwen #OpenAI #AIDevelopment #SoftwareEngineering #CodeReview #Automation #AITools #TechTutorial #Programming #Developer #Coding #AIProgramming #AutonomousAI #CodeAgent #TechEducation