AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

ArXi:2605.15206v1 Announce Type: cross Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based question answering. While remote, cloud-based agents offer scalability and ease of deployment, they raise privacy concerns, depend on network connectivity, and incur recurring API costs. Deploying agents locally on user devices mitigates these issues by preserving data privacy and eliminating usage-based fees. However, agentic workflows are far resource-intensive than typical LLM interactions.