# OOPMAS

Automated design of multi-agent systems has mostly worked at the task level, optimizing one fixed workflow per benchmark and applying it to every query. OOPMAS moves design to the individual query: a frozen LLM as meta-agent first generates object-style agents with roles, tools and persistent state, then an executable main-function workflow, while a dynamic skill library accumulates structured lessons from execution feedback without gradient updates. On a benchmark mixing code, math and QA, OOPMAS reaches 89.6% overall accuracy, 18.1 percentage points above the strongest baseline, ADAS.

Automated design of multi-agent systems has mostly worked at the task level, optimizing one fixed workflow per benchmark and applying it to every query. OOPMAS moves design to the individual query: a frozen LLM as meta-agent first generates object-style agents with roles, tools and persistent state, then an executable main-function workflow, while a dynamic skill library accumulates structured lessons from execution feedback without gradient updates. On a benchmark mixing code, math and QA, OOPMAS reaches 89.6% overall accuracy, 18.1 percentage points above the strongest baseline, ADAS.