Effective harnesses for long-running agents

What this post added

This post introduces a two-fold solution to enable long-running AI agents to make consistent progress across multiple context windows: an initializer agent for environment setup and a coding agent for incremental progress. It details strategies to overcome challenges like agents attempting to do too much at once or prematurely declaring tasks complete. Key techniques include using a feature requirements file (initially marked as 'failing'), prompting agents to work on one feature at a time, leaving the environment in a clean state with git commits and progress summaries, and employing browser automation tools for end-to-end testing. The post also discusses open questions regarding single vs. multi-agent architectures and generalizing these techniques to other domains.