AURORA curbs AI context bloat in long sessions

Solving context bloat is the holy grail for persistent AI agents, and AURORA's flat prompt-load profile is a highly compelling technical signal.

• –Most multi-agent systems eventually collapse under their own context weight or suffer from severe instruction drift over long sessions.
• –A flat prompt growth curve implies an active memory management or state-decay approach rather than simple context window accumulation.
• –If this architectural pattern holds true at scale, it could drastically lower inference costs for continuous workflows.
• –The project is still an early experiment by a solo developer, seeking technical validation rather than announcing a robust commercial launch.