SALVAL

Software development is becoming increasingly complex, especially for students, beginner developers, and small development teams working with unfamiliar or rapidly growing codebases. Developers often start making changes without fully understanding the existing project structure, dependencies, or relationships between files. This can lead to inefficient debugging, inconsistent code, unnecessary rework, and new errors being introduced while trying to solve existing ones. Traditional AI coding assistants can generate code quickly, but they may focus on the immediate request without first understanding the bigger picture of the project. **SALVAL** addresses this problem by acting as an AI development assistant that analyzes a codebase before helping developers continue building it. Instead of relying on a single general-purpose AI response, SALVAL uses a **main AI agent supported by specialized subagents**, each focused on a specific development task. The system can analyze the project's structure and delegate tasks to agents responsible for areas such as **code debugging, code review and fixes, UI development, and project structure analysis**. The agents work together to provide more context-aware assistance rather than treating every coding request as an isolated problem. The target users are **students, beginner developers, individual developers, and small development teams** who need assistance understanding, maintaining, debugging, or extending software projects. Users interact with SALVAL through its web interface by providing their project/codebase and describing what they want to accomplish. The system analyzes the project, identifies relevant files and issues, and uses the appropriate specialized agent to assist with the task. What makes SALVAL unique is its **multi-agent development workflow**. Rather than simply asking an AI to “write code,” SALVAL first focuses on understanding the existing system and then coordinates specialized agents according to the task. This creates a workflow closer to having a small AI development team: one agent can understand the structure, another can investigate bugs, another can review or improve code, while another can focus on the interface. By combining codebase awareness, task-specific agents, and coordinated AI assistance, SALVAL aims to reduce the time developers spend searching through files, diagnosing problems, and repeatedly fixing AI-generated code. The result is a more structured development process that helps users move from **“What is happening in my project?” to “Here is what needs to be changed and how we can build it.”**