Abstract
Generative artificial intelligence agents engaged in long-duration tasks often suffer from "contextual fatigue," a noticeable decline in reasoning quality and focus over time. To resolve this issue, an automated monitoring system can track the AI's internal word-choice patterns to detect early statistical signs of performance decay. Once this fatigue is identified, the system triggers a recovery process that temporarily pauses the main AI for a "sleep cycle." During this brief rest, a secondary AI reviews and condenses the primary agent's cluttered memory into a clean, dense summary. By resuming its task with this newly compressed information, the primary AI regains its sharpness, allowing for sustained focus and coherent performance over extended periods.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Raithatha, Deep and Kayande, Tanmay, "Automatically Managing AI Fatigue Through Performance Tracking and Memory Summaries", Technical Disclosure Commons, (May 25, 2026)
https://www.tdcommons.org/dpubs_series/10227