Standard chat treats context as knowledge - what’s in the window is what the model knows.
This system treats context as attention - the window holds what the model is currently attending to, while understanding persists in a structured, curated representation.
The scratchpad is not a log of what happened. It’s a live model of current understanding.
A system for evaluating whether AI agents actually learn.
A typical framing of AI agent evaluation focuses on task completion: did the agent produce the right output? And as it goes with these framings, the word "right" is doing enormous work. Task completion tells you whether an agent succeeded once. It says nothing about whether the agent will succeed better next time.
Rigorously evaluate the tether plugin through diverse tasks, tracking workflow fidelity, workspace quality, and friction points to inform its evolution. Design Goals:
workspace/ for analysis
Validate data flow pathways and implementation approaches before code execution, ensuring simplicity, pattern compliance, and TDD-ready specifications.