Multifaceted Guidance, Targeted Correction: Experience Harness for LLM Agents

LLM agents can improve future behavior by reusing experience from past task executions. However, such reuse becomes challenging in realistic complex tasks involving sequentially dependent bottlenecks that differ in focus and are progressively revealed during execution. Consequently, generic reuse may overemphasize certain bottleneck focuses while overlooking others, or fail to provide timely intervention when errors emerge during execution. In this paper, we introduce Experience Harness, a specialized external control method that organizes experience from successful and failed trajectories and regulates how that experience guides and corrects an LLM agent. Specifically, it decomposes trajectories into bottleneck-aware atoms along four dimensions, inducing success skills for multifaceted task-start guidance and failure modes that trigger targeted corrective intervention during execution. This design preserves both the bottleneck focus of each experience and the stage at which it should take effect. Across multiple base agents on the AppWorld and BFCL-V3 benchmarks, our method consistently outperforms existing experience-memory baselines. Detailed experimental analyses further confirm the value of multifaceted guidance across bottleneck focuses and targeted correction at appropriate execution stages.