Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning. Existing approaches, however, rely on static agent behaviors and fixed orchestration strategies, leading to brittle performance on diverse, multi-hop tasks. We identify two key limitations: the lack of continuously adaptive orchestration mechanisms and the absence of behavior-level learning for individual agents. To this end, we propose HERA, a hierarchical framework that jointly evolves multi-agent orchestration and role-specific agent prompts. At the global level, HERA optimizes query-specific agent topologies through reward-guided sampling and experience accumulation. At the local level, Role-Aware Prompt Evolution refines agent behaviors via credit assignment and dual-axes adaptation along operational and behavioral principles, enabling targeted, role-conditioned improvements. On six knowledge-intensive benchmarks, HERA achieves an average improvement of 38.69\% over recent baselines while maintaining robust generalization and token efficiency. Topological analyses reveal emergent self-organization, where sparse exploration yields compact, high-utility multi-agent networks, demonstrating both efficient coordination and robust reasoning.

翻译：多智能体检索增强生成（RAG）中，各智能体承担特定角色以支持需要多步骤、多数据源或复杂推理的难题。然而现有方法依赖静态智能体行为与固定编排策略，导致多样化的多跳任务中表现脆弱。我们识别出两个关键局限：缺乏持续自适应编排机制，以及缺乏针对个体智能体的行为层级学习。为此提出层级框架HERA，协同进化多智能体编排与角色特定智能体提示。在全局层面，HERA通过奖励引导采样与经验积累优化查询特定智能体拓扑结构；在局部层面，角色感知提示进化通过信用分配与操作-行为双轴适配机制精炼智能体行为，实现目标导向的角色条件化改进。在六个知识密集型基准测试中，HERA较近期基线方法平均提升38.69%，同时保持强泛化能力与token效率。拓扑分析揭示了涌现式自组织现象，稀疏探索即可生成紧凑高效的多智能体网络，展现出高效协调与稳健推理双重优势。