Agent-HAnS: Maintaining Feature and Code Traceability in Agentic Development

Abstract

Agentic coding tools using LLMs enable developers to quickly generate software. However, developers quickly lose the overview over their increasingly complex codebases. During code generation, no knowledge about what features exist or where they are implemented is documented. We present Agent-HAnS, an agentic workflow which instructs the LLM to update a feature model and generate embedded feature annotations after each code-generation step. It visualizes the evolving structure on a live dashboard served via a Model Context Protocol server. We demonstrate Agent-HAnS on a C#/.NET 9 web API evolved through 27 prompts across 10 evolution steps, with most annotations maintained automatically and no active annotation effort from the developer. In a manual audit, 92% of fragment annotations and 70% of file mappings remain correct, with errors concentrating at feature merges and splits. In a user study, Agent-HAnS scored 73.9 on the System Usability Scale. Tool video: https://youtu.be/Nb-eDrGokI8