Long-Running Agent Framework

Framework for enabling AI agents to work effectively across many context windows on complex tasks.

Core Problem

Long-running agents must work in discrete sessions where each new session begins with no memory of previous work. Without proper scaffolding, agents tend to:

1. One-shot attempts - Try to complete everything at once, running out of context mid-implementation
2. Premature completion - See partial progress and declare the job done
3. Undocumented states - Leave code in broken or undocumented states between sessions

Two-Agent Solution

1. Initializer Agent (First Session Only)

Sets up the environment with all context future agents need: