Dev Uses AI-Assisted Stress Testing to Catch Bugs Before Users Do

A software developer and former clinician describes how years of manual application testing shaped a rigorous approach to validation, including using AI models from multiple companies to generate edge-case scenarios. The habit began during a job as an application analyst, where he spent months manually timing image-load performance on a PACS system to document a problem users could only describe anecdotally. He found measurable degradation but no clear pattern — a result he considers equally valuable, as it prevented false conclusions. Today he deliberately prompts AI tools to surface unexpected inputs and failure modes rather than asking them to judge overall quality. This philosophy led him to build a dedicated tool called ReliAgent, designed to address the unique reliability challenges of AI agent systems where components may function correctly yet still produce untrustworthy results.