Maestro: Joint Graph & Config Optimization for Reliable AI Agents
This paper introruces **Maestro**, a novel, holistic optimization framework for Large Language Model (LLM) agents. Maestro is designed to improve agent reliability and performance by **jointly optimizing two dimensions**: the agent's structural **graph** (module flow and architecture) and its operational **configurations** (prompts, models, and tools). Unlike prior optimizers that fix the graph, Maestro employs an alternating block-coordinate scheme, guided by both numerical scores and reflective textual feedback from execution traces, to achieve **sample-efficient improvements**. Empirical results on benchmarks like HotpotQA and IFBench, as well as on interviewer and RAG applications, demonstrate that Maestro consistently **outperforms leading configuration-only optimizers** by addressing structural limitations and reducing the number of required experimental rollouts.