How a Forensic AI Assistant Uncovered Hidden Issues in a Software Sprint

This article describes how an AI agent, acting as a forensic assistant, discovered critical issues in a software codebase that the main development agent had not yet encountered, leading to a more efficient and successful sprint.

Details

The article describes a software development sprint where two AI agents were working in parallel - one as the main 'building' agent, and the other as a 'forensic' agent. The forensic agent's role was to thoroughly analyze the codebase, uncover hidden issues, and document them for the building agent to address. The forensic agent discovered several critical problems, including a 'split-brain' issue with the database files, and silent failures in lazy-loaded imports that were causing several API endpoints to return HTML instead of the expected JSON. By identifying and documenting these problems upfront, the forensic agent was able to save the building agent significant time and effort in debugging and resolving the issues. This approach of having a dedicated forensic agent analyze the codebase in parallel with the main development work allowed for a more efficient and successful sprint, as the building agent could focus on implementing the planned features and fixes without getting bogged down by unexpected problems.