LLM-based coding agents are achieving impressive performance on complex software engineering tasks, yet their high computational cost remains a major barrier to practical deployment. This talk focuses on improving efficiency for agent systems via turn control and trajectory reduction. The talk starts with an empirical study of turn-control strategies showing how simple prompts can reduce cost and sometimes improve success rates. Moving forward, I present how inference-time trajectory reduction can remove redundant or expired information to reduce token usage without harming performance. These findings highlight that smarter resource management is able to scalable agent systems. The talk concludes with future research opportunities toward efficiency-aware agent design.

Speaker Bio: Dr. Chao Peng is a Principal Research Scientist at ByteDance, where he leads the Software Engineering Lab focusing on AI agents for software engineering. His research interests include software testing, program repair and optimisation, as well as their synergy with machine learning and compiler techniques. His work has been published in premier venues such as ICSE, FSE, ASE, ACL, and NeurIPS. He received the Distinguished Reviewer Award at FSE 2025.

I am a Principal Research Scientist at ByteDance (字节跳动). I received my PhD degree from Laboratory for Foundations of Computer Science (LFCS), The University of Edinburgh under supervision of Dr. Ajitha Rajan.

At ByteDance, I lead the Trae Research team (ByteDance Software Engineering Lab), where we conduct research on AI agents for software engineering including the application and evaluation of AI agents, and training LLMs for agents. I am also responsible for academic development and university collaboration.

I am passionate about building practical software testing, analysis, and debugging systems to predict, detect, diagnose, and fix bugs for all kinds of software systems.

Outside of work, I enjoy going to the gym.