Title: Towards Robust Agentic Systems through Generative Flow Exploration of Primitives
Primary area: optimization
Keywords: Agentic system; Large Language Model; robustness of agentic system; Generative FlowNet

Abstract: The automated design of agentic systems has emerged as a key challenge for scaling large language models (LLMs) beyond single-agent reasoning. While prior work has advanced task performance through handcrafted or automatically generated multi-agent workflows, robustness remains largely treated as an afterthought, leaving systems vulnerable to external adversaries and internal failures.  We propose \textbf{AutoRAS}, a framework for the \textbf{Auto}mated design of \textbf{R}obust \textbf{A}gentic \textbf{S}ystems. The core idea is to represent system design as a sequence generation problem over symbolic \emph{primitives} that jointly encode structural connections and behavioral actions. This abstraction enables (i) principled construction of executable workflows, (ii) integration of dynamic \emph{safety signals} distilled from execution traces into the design loop, and (iii) flow-based optimization that propagates rewards across entire sequences to handle credit assignment and equifinality. Through this dual feedback channel, where numeric rewards guide exploration and textual signals refine behaviors, AutoRAS systematically improves both external resilience and internal reliability. Experiments on four datasets under four attack settings against 11 baselines, including handcrafted and automated designs, show that AutoRAS attains state-of-the-art results on three datasets and consistently exhibits the smallest performance drop after attacks (average $2.13$). Additional transfer, ablation, and sensitivity analyses further confirm the effectiveness of our design.