Timely skill guidance helps large language model agents solve tasks better
Enabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context
Artificial IntelligenceMultiagent Systems
Summary
When large language model agents try to solve complex tasks, they often reuse skills but get guidance too late, after making mistakes. The paper’s authors propose TipsWarm, a method that keeps useful hints from these skills ready to share before the agent starts working, so it can avoid bad approaches early. TipsWarm manages these hints efficiently, so they don't slow down the agent or show unhelpful advice repeatedly. Tests show TipsWarm helps agents complete tasks more successfully and quickly than previous methods.
What this means in practice
- •For software developers: Improve coding assistants by providing timely skill hints before starting new code, increasing success on complex programming tasks.
- •For workflow automation teams: Enhance task automation agents with early access to good practices, reducing costly mistakes and improving task completion rates.
Authors
Feng Liang, Yupeng Li, Runhao Zeng, Francis C. M. Lau, Xiping Hu
Abstract
Reusable skills help LLM-based agents solve complex tasks, but the agent must receive guidance before it commits to an ineffective approach. Existing skill mechanisms often expose only metadata and load full content on demand, leaving useful guidance unavailable until the agent decides to retrieve it. General memory methods can incur substantial maintenance overhead, while keeping guidance in conversation context risks repeatedly exposing the agent to irrelevant or harmful advice. We propose TipsWarm, a mechanism that complements existing skill mechanisms by maintaining a budgeted pool of skill-derived keypoints, or \textit{warm tips}, for selective injection into the context of every message turn. By separating event-triggered LLM assessment from inexpensive per-turn screening, it makes transferable skill guidance readily available while controlling maintenance costs. In three coding and iterative task-execution benchmarks, TipsWarm achieves the highest task success rate while remaining time-efficient, compared to recent skill and general memory baselines.