Title: MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: multi agent system, agentic workflow

Abstract: Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLM-driven construction. We propose MermaidFlow, a framework that redefines the agentic search space through safety-constrained graph evolution. At its core, MermaidFlow represent workflows as a verifiable intermediate representation using Mermaid, a structured and human-interpretable graph language. We formulate domain-aware evolutionary operators, i.e., crossover,  mutation, insertion, and deletion, to preserve semantic correctness while promoting structural diversity, enabling efficient exploration of a high-quality, statically verifiable workflow space. Without modifying task settings or evaluation protocols, MermaidFlow achieves consistent improvements in success rates and faster convergence to executable plans on the agent reasoning benchmark. The experimental results demonstrate that safety-constrained graph evolution offers a scalable, modular foundation for robust and interpretable agentic reasoning systems.

TL;DR: MermaidFlow ensures safer, more reliable workflow generation by evolving verifiable Mermaid graphs, boosting success rates, convergence speed, and interpretability.