Adaptive Multi-Agent Pool Evolution: An Online Selection and Generation Framework

Existing single-agent systems typically rely on fixed policies or Bayesian posterior updates to handle diverse tasks during execution. However, fixed policies lack flexibility, while Bayesian methods incur high computational overhead and converge slowly under frequent task switching. To overcome these limitations, this paper proposes AMAPE (Adaptive Multi-Agent Pool Evolution)—an online selection and generation framework that dynamically maintains an evolvable pool of sub-agents. For each incoming task, the system generates sub-agents tailored to its characteristics and caches them in the pool for direct reuse by similar tasks in the future. The framework employs the Wilson confidence interval to monitor the global execution success rate online: when the confidence lower bound falls below a preset threshold, the system triggers the generation of a new agent by synthesizing the current task description, prior experience from similar successful tasks, and failure avoidance strategies distilled from historical failures. For agent selection, we design a composite metric that integrates task similarity with each agent's Upper Confidence Bound (UCB) score, effectively balancing the exploration of newly generated agents and the exploitation of proven high-performing ones. Experimental results demonstrate that AMAPE dynamically expands and updates the agent pool in response to evolving task distributions, improving task success rates without introducing additional execution overhead.