AI RESEARCH
AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
arXiv CS.LG
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ArXi:2604.06296v1 Announce Type: new AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on \emph{server-side} efficiency, proposing methods such as caching, speculative execution, traffic scheduling, and load balancing to reduce the cost of serving agentic workloads. However, as users increasingly construct agents by composing local tools, remote APIs, and diverse models, an equally important optimization problem arises on the client side.