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Reducing Cost of LLM Agents with Trajectory Reduction
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Abstract
Multi-turn agent systems based on Large Language Models (LLMs) have become increasingly popular for software engineering tasks. While LLM agents demonstrate promising effectiveness, the high computational cost of input tokens due to ever-growing trajectories remains a significant efficiency concern. Efficiency has been largely overlooked in existing studies and agent products, and this paper addresses this gap by introducing an inference-time trajectory reduction approach that reduces computational costs. By analyzing existing agent trajectories, we demonstrate that useless, redundant, and expired information is widespread across trajectories. Such waste can be identified and reduced without compromising the agent's performance. We propose a simple yet effective trajectory reduction approach, AgentDiet, which automatically removes such waste during agent execution. We implement AgentDiet on a top-performing coding agent, and our evaluation on two LLMs and two benchmarks shows that AgentDiet can reduce input tokens by 39.9%-59.7% and the total computational cost by 21.1%-35.9%, while maintaining the same agent performance. These results indicate that inference-time trajectory reduction is a promising direction for agent systems.
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Cites
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Cited by
Memory in the Age of AI Agents
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Context as a Tool: Context Management for Long-Horizon SWE-Agents
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Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation
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SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
Wang, Y., Shi, Y., Yang, M., Zhang, R., He, S., Lian, H., Chen, Y., Ye, S., Cai, K., & Gu, X. (2026) · arXiv · doi:10.48550/arxiv.2601.16746 · Cited by 2
“…in software engineering tasks, from simple code understanding (Chen et al., 2021; Li et al., 2023b; Yang et al., 2025; Hu et al., 2025) to interactive agents capable of navigating repositories, running tests, and submitting patches end-to-end (Chen et al., 2025; Li et al., 2025; Xiao et al., 2025).”
Metadata
- Authors
- Xiao, Y.-A., Gao, P., Peng, C., & Xiong, Y.
- Venue
- Proceedings of the ACM on Software Engineering
- Published
- 2026-06-30
- Volume
- 3
- Issue
- FSE
- Pages
- 1241-1263
- ISSN
- 2994-970X
- Cited by
- 6
- References
- 9
- Language
- en
- Funders
- National Natural Science Foundation of China (92582202)
- National Key Research and Development Program of China (2022YFB4501902)
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