Agentic AI enables LLM to dynamically reason, plan, and interact with tools to solve complex tasks. However, agentic workflows often require many iterative reasoning steps and tool invocations, leading to significant operational expense, end-to-end latency and failures due to hallucinations. This work introduces Agent Workflow Optimization (AWO), a framework that identifies and optimizes redundant tool execution patterns to improve the efficiency and robustness of agentic workflows. AWO analyzes existing workflow traces to discover recurring sequences of tool calls and transforms them into meta-tools, which are deterministic, composite tools that bundle multiple agent actions into a single invocation. Meta-tools bypass unnecessary intermediate LLM reasoning steps and reduce operational cost while also shortening execution paths, leading to fewer failures. Experiments on two agentic AI benchmarks show that AWO reduces the number of LLM calls up to 11.9% while also increasing the task success rate by up to 4.2 percent points.

翻译：智能体AI使LLM能够动态推理、规划并与工具交互以解决复杂任务。然而，智能体工作流通常需要大量迭代推理步骤和工具调用，导致显著的操作开销、端到端延迟以及因幻觉而产生的故障。本文提出智能体工作流优化框架，该框架通过识别并优化冗余工具执行模式来提升智能体工作流的效率与鲁棒性。AWO通过分析现有工作流轨迹，发现重复出现的工具调用序列，并将其转化为元工具——一种将多个智能体动作捆绑为单次调用的确定性复合工具。元工具绕过了不必要的中间LLM推理步骤，在降低操作成本的同时缩短执行路径，从而减少故障发生。在两个智能体AI基准测试上的实验表明，AWO将LLM调用次数降低达11.9%，同时将任务成功率提升达4.2个百分点。