Why My Audit Agent Needed Hindsight, Not More Prompts
A dark-pattern detector can find a suspicious button today. The harder problem is making sure it remembers why a human reviewer said that button was harmless yesterday---and still notices when a previously fixed problem comes back. I built an audit agent around that problem. It analyzes online-shop pages for five classes of dark patterns, lets a human reviewer confirm or reject findings, and uses…
An audit agent designed to detect deceptive practices on e-commerce websites requires more than just a one-time analysis. The challenge lies in remembering past decisions and adapting to changes over time. I constructed an audit system that addresses this issue through persistent memory, allowing it to learn from previous audits and apply that knowledge to future evaluations.
By incorporating historical context before conducting an assessment and retaining reviewer decisions for future reference, the system can identify both new and recurring problems more accurately.
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