Agents meet OKR: An Object and Key Results Driven Agent System with
  Hierarchical Self-Collaboration and Self-Evaluation
Yi Zheng Chongyang Ma Kanle Shi Haibin Huang
In this study, we introduce the concept of OKR-Agent designed to enhance the
capabilities of Large Language Models (LLMs) in task-solving. Our approach
utilizes both self-collaboration and self-correction mechanism, facilitated by
hierarchical agents, to address the inherent complexities in task-solving. Our
key observations are two-fold: first, effective task-solving demands in-depth
domain knowledge and intricate reasoning, for which deploying specialized
agents for individual sub-tasks can markedly enhance LLM performance. Second,
task-solving intrinsically adheres to a hierarchical execution structure,
comprising both high-level strategic planning and detailed task execution.
Towards this end, our OKR-Agent paradigm aligns closely with this hierarchical
structure, promising enhanced efficacy and adaptability across a range of
scenarios. Specifically, our framework includes two novel modules: hierarchical
Objects and Key Results generation and multi-level evaluation, each
contributing to more efficient and robust task-solving. In practical,
hierarchical OKR generation decomposes Objects into multiple sub-Objects and
assigns new agents based on key results and agent responsibilities. These
agents subsequently elaborate on their designated tasks and may further
decompose them as necessary. Such generation operates recursively and
hierarchically, culminating in a comprehensive set of detailed solutions. The
multi-level evaluation module of OKR-Agent refines solution by leveraging
feedback from all associated agents, optimizing each step of the process. This
ensures solution is accurate, practical, and effectively address intricate task
requirements, enhancing the overall reliability and quality of the outcome.
Experimental results also show our method outperforms the previous methods on
several tasks. Code and demo are available at https://okr-agent.github.io/