Deploying Large Language Model (LLM)-based AI agents at the network edge enables real-time task handling in 6G networks. However, resource heterogeneity and user mobility necessitate agent migration to maintain Quality of Service (QoS). Unlike stateless containers, AI agents encapsulate invocation histories, planning contexts, and memory stores, making their migration considerably more complex. To address this issue, we propose AMD, an adaptive AI Agent Migration framework that integrates conditional Diffusion models with reinforcement learning to jointly optimize latency and resource utilization under dynamic edge environments. Specifically, we introduce a diffusion model as a generative prior to produce high-quality global deployment plans, effectively avoiding the local-optima problem common in conventional reinforcement learning. A reinforcement learning-based module then performs online policy refinement to accommodate real-time environmental variations. We implement AMD on a distributed system built upon AgentScope and validate it across geographically distributed edge servers. Experimental results demonstrate that AMD reduces the average task latency by 3.9%–27.6% and improves resource utilization by up to 53.3% compared to baseline strategies.
Jie Gao, Xingdan Wang, Zhiqing Tang et al.· Tsinghua Science and Technol...· 0 citations
Agentic workflows, composed of multiple collaborating Large Language Models (LLMs), have become a key paradigm for complex problem-solving. However, their effectiveness is often hindered by three critical challenges: high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences. To address these limitations, we propose HFlow, an evolutionary framework for generating agentic workflows through human-agent collaboration. HFlow employs an evolutionary algorithm to automate the search for optimal workflows by mutating and crossing over their structures, prompts, and LLM backbones. This process is guided by human preferences to ensure rapid convergence, while a hierarchical experience memory enables the generalization of learned strategies. Extensive experiments on math and code generation benchmarks show HFlow surpasses other automated baselines by up to 27.34%, while achieving comparable performance to o1-preview at only one-fourth of the cost. Our work introduces a new paradigm for workflow design that produces cost-effective and adaptive solutions, better aligning automated agen-tic systems with dynamic human needs.
Yuxin Liu, Jinxuan Zhang, Yuezhang Peng et al.· Annual Meeting of the Associ...· 1 citation