SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering

2026-06-30

AI Summary

In long-horizon tasks, LLM agents frequently trigger KV cache invalidation due to context engineering techniques such as offloading and compression, leading to substantial increases in first-token latency. This work reveals, for the first time, that context transitions exhibit segment-wise decomposability. Building on this insight, the authors propose a proactive programming model that asynchronously pre-executes transition operations and introduce an interference-aware scheduler. Without modifying existing agent logic, this approach proactively generates target KV caches, enabling seamless, non-blocking context replacement. Integrated into mainstream agent frameworks and LLM serving systems, the method reduces first-token latency by up to 11.9× and entirely eliminates the performance overhead associated with context transitions.

Abstract

LLM-based agents execute multi-turn workflows with continuously growing contexts, where LLM calls are interleaved with tool invocations and environment feedback. To maintain model quality, modern agent frameworks rely on context engineering strategies such as offloading, reduction, and isolation to control the context length. However, these strategies introduce significant context transformation overhead: each transformation invalidates existing KV caches and triggers re-prefill, leading to increased time-to-first-token (TTFT). In this paper, we identify that context transformations are segment-decomposable, where the transformation of a prefix is independent of future tokens. This property enables transformations to be executed ahead of time. Based on this insight, we propose a lookahead programming model that allows agent frameworks to express context transformations as asynchronous operations without modifying their execution logic. The runtime proactively executes these transformations and prepares transformed KV caches in advance, enabling direct context replacement without blocking. We further design a lookahead-aware scheduler in LLM serving systems to support these asynchronous requests alongside latency-critical workloads with controlled interference. We implement our approach to support representative context engineering strategies and integrate it into existing agent frameworks and LLM serving systems. Experiments show that our approach effectively eliminates transformation overhead and reduces TTFT by up to 11.9x.

Problem

• LLM-based agents
• context engineering
• KV cache invalidation
• time-to-first-token
• long-horizon serving

Innovation

• lookahead context engineering
• KV cache reuse
• asynchronous transformation
• LLM-based agents
• context management