Artem Getman

Current AI agents can improve within one chat, then forget everything in the next. Cortex solves that by giving agents persistent memory that learns from failures automatically. It captures runtime mistakes (tool errors, wrong syntax, bad action choices), turns them into structured lessons, and retrieves the most relevant lessons by domain/task context during future runs. Lessons are outcome-scored: helpful lessons are reused/promoted, harmful ones are suppressed. In our mixed benchmark (gridtool -> fluxtool -> shell/excel -> sqlite -> grid retention), Wave 1 starts cold and fails multiple tasks; Waves 2–3 recover with higher pass rates, fewer errors, and fewer steps. This moves agents from stateless chat behavior toward cumulative learning from real execution history.