REMaster is a long-horizon AI team manager designed to operate across weeks or months without drowning in its own history.
We built a two-level architecture. Individual agents understand each teammate—their work, skills, blockers and availability. Above them, a Strategist manages the entire team from goal to completion, continuously planning and adapting as reality changes.
The core challenge is memory. Long-running agents either keep everything until context becomes too large, or summarize history and risk permanently losing details that become important later.
REMaster makes forgetting reversible.
Its working context is compressed through explicit keep, fold, promote and archive operations, while the complete history remains preserved.
Most importantly, REMaster checks its own forgetting. Lagged Shadow agents retain slightly different memory. If a Shadow makes a meaningfully different decision, REMaster identifies what missing context caused the divergence, restores it, and re-evaluates the decision.
In our demo, a teammate mentions in Week 2 that she will be away for her wedding in Week 9. Weeks later, that fact leaves active context. When REMaster assigns her critical Week 9 work, a Shadow disagrees. REMaster finds the forgotten wedding, restores it, and fixes the plan.
The goal isn't AI that remembers everything. It's AI that knows what matters—and knows when it forgot the wrong thing.
