Autonomous agents driven by Large Language Models (LLMs) offer enormous
potential for automation. Early proof of this technology can be found in
various demonstrations of agents solving complex tasks, interacting with
external systems to augment their knowledge, and triggering actions. In
particular, workflows involving multiple agents solving complex tasks in a
collaborative fashion exemplify their capacity to operate in less strict and
less well-defined environments. Thus, a multi-agent approach has great
potential for serving as a backbone in many industrial applications, ranging
from complex knowledge retrieval systems to next generation robotic process
automation. Given the reasoning abilities within the current generation of
LLMs, complex processes require a multi-step approach that includes a plan of
well-defined and modular tasks. Depending on the level of complexity, these
tasks can be executed either by a single agent or a group of agents. In this
work, we focus on designing a flexible agent engineering framework with careful
attention to planning and execution, capable of handling complex use case
applications across various domains. The proposed framework provides
reliability in industrial applications and presents techniques to ensure a
scalable, flexible, and collaborative workflow for multiple autonomous agents
working together towards solving tasks.

设计了一个灵活的智能体工程框架，着重规划和执行，适用于多个领域的复杂应用，提供可靠性的工业应用，并提出了确保多个自主智能体共同解决任务的可扩展、灵活和协作式工作流技术。