  Large language model agents have demonstrated remarkable advancements across
various complex tasks. Recent works focus on optimizing the agent team or
employing self-reflection to iteratively solve complex tasks. Since these
agents are all based on the same LLM, only conducting self-evaluation or
removing underperforming agents does not substantively enhance the capability
of the agents. We argue that a comprehensive evaluation and accumulating
experience from evaluation feedback is an effective approach to improving
system performance. In this paper, we propose Reusable Experience Accumulation
with 360$^\circ$ Assessment (360$^\circ$REA), a hierarchical multi-agent
framework inspired by corporate organizational practices. The framework employs
a novel 360$^\circ$ performance assessment method for multi-perspective
performance evaluation with fine-grained assessment. To enhance the capability
of agents in addressing complex tasks, we introduce dual-level experience pool
for agents to accumulate experience through fine-grained assessment. Extensive
experiments on complex task datasets demonstrate the effectiveness of
360$^\circ$REA.
