Why agents need sandboxes, not larger context windows

This post introduces the concept of agent sandboxes as a solution to the limitations of LLM context windows for long-running agent tasks. It details how sandboxes provide agents with a virtual computer environment (file system, command line, code runtime, tool index) to expand their effective short-term memory. The post explains how sandboxes enable agents to process larger datasets, perform complex analyses (e.g., pipeline health analysis, sensitivity analysis), and overcome context overload by storing data on the filesystem for on-demand access. It also discusses the engineering lessons learned from previous memory management strategies and highlights the security aspects of sandboxes, including isolation, resource limits, and permission inheritance. The core technical contribution is the architectural shift from context window truncation to persistent storage in a sandboxed environment for agent memory management.