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In this study, we introduce the concept of OKR-Agent designed to enhance thecapabilities of Large Language Models (LLMs) in task-solving. Our approachutilizes both self-collaboration and self-correction mechanism, facilitated byhierarchical agents, to address the inherent complexities in task-solving. Ourkey observations are two-fold: first, effective task-solving demands in-depthdomain knowledge and intricate reasoning, for which deploying specializedagents 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 hierarchicalstructure, promising enhanced efficacy and adaptability across a range ofscenarios. Specifically, our framework includes two novel modules: hierarchicalObjects and Key Results generation and multi-level evaluation, eachcontributing to more efficient and robust task-solving. In practical,hierarchical OKR generation decomposes Objects into multiple sub-Objects andassigns new agents based on key results and agent responsibilities. Theseagents subsequently elaborate on their designated tasks and may furtherdecompose them as necessary. Such generation operates recursively andhierarchically, culminating in a comprehensive set of detailed solutions. Themulti-level evaluation module of OKR-Agent refines solution by leveragingfeedback from all associated agents, optimizing each step of the process. Thisensures solution is accurate, practical, and effectively address intricate taskrequirements, enhancing the overall reliability and quality of the outcome.Experimental results also show our method outperforms the previous methods onseveral tasks. Code and demo are available at https://okr-agent.github.io/

Agents meet OKR: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation