Imagine software that fixes itself, learns from its mistakes, and constantly evolves without human intervention. This isn't science fiction, but the exciting potential of agent-driven automatic software improvement, a field exploring how AI agents can revolutionize software development. Software maintenance currently gobbles up 50% of development costs—a significant drain on resources and a constant source of frustration. The rise of Large Language Models (LLMs) has offered tantalizing possibilities for automating coding tasks, but even LLMs stumble, producing bugs and vulnerabilities. This is where AI agents step in. These aren't just passive code-generation tools; they're active learners. They interact with the software environment, identify areas for improvement, and iterate through solutions, much like human developers but at an accelerated pace. This research delves into how these LLM-powered agents can tackle the 'last mile' problems of code generation—those pesky errors that pop up at the final stages. The project also envisions a collaborative framework where multiple agents, each specializing in a different aspect of software engineering (security, bug detection, optimization), work together, learning from each other’s strengths and weaknesses. This collaborative approach could unlock solutions to coding challenges that have stumped traditional methods. Moreover, the constant feedback loop within these agent systems generates valuable data, potentially leading to innovative fine-tuning techniques for LLMs themselves, making them even more powerful and efficient code generators. The potential benefits are immense: reduced development costs, faster turnaround times, and more reliable, secure software. While significant challenges remain in orchestrating these complex multi-agent systems and developing robust evaluation metrics, the potential of self-improving software promises a future where AI transforms not just how we write code, but how software itself evolves.