The post explores the limitations of current LLM agents in performing long-running monitoring tasks, such as checking emails or tracking prices. It introduces SentinelStep, a solution designed to enhance agent capabilities by managing polling frequency and context overflow effectively. SentinelStep enables agents to dynamically adjust their polling strategies based on the specific requirements of the task, ensuring efficiency and reliability over extended periods.
SentinelBench is also introduced, providing synthetic web environments that can be used to evaluate monitoring tasks systematically. Preliminary tests of SentinelStep show improved success rates for longer tasks, increasing reliability significantly. The implementation of these technologies in Magentic-UI aims to create proactive agents that can monitor without wasting resources, providing a foundation for always-on assistants that are aligned with user intent and adaptable to real-world needs.
👉 Pročitaj original: Microsoft Research AI
