Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions

Understand

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.