Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards practical adoption of LLMs, multi-agent systems hold great promise to augment, integrate, and orchestrate LLMs in the larger context of enterprise platforms that use existing proprietary data and models to tackle complex real-world tasks. Despite the tremendous success of these systems, current approaches rely on narrow, single-focus objectives for optimization and evaluation, often overlooking potential constraints in real-world scenarios, including restricted budgets, resources and time. Furthermore, interpreting, analyzing, and debugging these systems requires different components to be evaluated in relation to one another. This demand is currently not feasible with existing methodologies. In this postion paper, we introduce the concept of reasoning capacity as a unifying criterion to enable integration of constraints during optimization and establish connections among different components within the system, which also enable a more holistic and comprehensive approach to evaluation. We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system. We then argue how these limitations can be addressed with a self-reflective process wherein human-feedback is used to alleviate shortcomings in reasoning and enhance overall consistency of the system.

翻译：大语言模型（LLMs）在各种任务中的卓越表现，既带来了诸多机遇，也带来了在生产环境中应用它们的挑战。为了推动LLMs的实际应用，多智能体系统在整合、集成和编排LLMs方面展现出巨大潜力，尤其是在利用现有专有数据和模型处理复杂真实任务的企业平台中。尽管这些系统取得了巨大成功，但当前方法依赖于狭窄、单一目标的优化和评估，往往忽视了现实场景中的潜在约束，包括有限的预算、资源和时间。此外，解读、分析和调试这些系统需要评估不同组件之间的相互关系，而现有方法尚无法满足这一需求。在这篇立场论文中，我们引入“推理能力”这一概念作为统一标准，以便在优化过程中整合约束，并建立系统内不同组件之间的联系，从而实现更全面、更综合的评估方法。我们给出了推理能力的形式化定义，并阐明了其在识别系统各组件局限性方面的实用性。随后，我们论证了如何通过自我反思过程来解决这些局限性，在这一过程中，利用人类反馈来弥补推理不足，并增强系统的整体一致性。