# Adaptive AI Agent Placement and Migration in Edge Intelligence Systems

arXiv: https://arxiv.org/abs/2508.03345 · HTML: https://mlsanity.com/paper/2508.03345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 6 of 6)

- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://mlsanity.com/paper/2402.14034/) (2024)
- [A Survey on Edge Computing Systems and Tools](https://mlsanity.com/paper/1911.02794/) (2019)
- [Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing](https://mlsanity.com/paper/1910.07990/) (2019)
- [AI Augmented Edge and Fog Computing: Trends and Challenges](https://mlsanity.com/paper/2208.00761/) (2022)
- [Multi-Agent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing](https://mlsanity.com/paper/2206.12146/) (2022)

## Built on by (most cited first, 3 of 3)

- [AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production](https://mlsanity.com/paper/2509.14647/) (2025)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation](https://mlsanity.com/paper/2607.22400/) (2026)

## Abstract (arXiv)

The rise of LLMs such as ChatGPT and Claude fuels the need for AI agents capable of real-time task handling. However, migrating data-intensive, multi-modal edge workloads to cloud data centers, traditionally used for agent deployment, introduces significant latency. Deploying AI agents at the edge improves efficiency and reduces latency. However, edge environments present challenges due to limited and heterogeneous resources. Maintaining QoS for mobile users necessitates agent migration, which is complicated by the complexity of AI agents coordinating LLMs, task planning, memory, and external tools. This paper presents the first systematic deployment and management solution for LLM-based AI agents in dynamic edge environments. We propose a novel adaptive framework for AI agent placement and migration in edge intelligence systems. Our approach models resource constraints and latency/cost, leveraging ant colony algorithms and LLM-based optimization for efficient decision-making. It autonomously places agents to optimize resource utilization and QoS and enables lightweight agent migration by transferring only essential state. Implemented on a distributed system using AgentScope and validated across globally distributed edge servers, our solution significantly reduces deployment latency and migration costs.