Enabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context

2026-09-26

AI Summary

This study addresses the problem of agents becoming trapped in unproductive trajectories due to the absence of immediate guidance prior to skill retrieval, and proposes the TipsWarm mechanism. This approach maintains a critical tip pool under budget constraints, leveraging context window management and dynamic injection techniques to achieve turn-wise selective injection of transferable skill guidance. Furthermore, it innovatively decouples event-triggered large language model (LLM) evaluation from low-cost filtering, effectively balancing guidance availability against maintenance overhead. Experimental results demonstrate that the proposed method achieves state-of-the-art task success rates on benchmarks such as coding tasks while maintaining efficient time performance.

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.

Problem

• LLM-based agents
• skill retrieval
• context management
• timely guidance
• memory overhead

Innovation

• Skill Retrieval
• Warm Tips
• Context Injection
• Budgeted Pool
• LLM Agents