Abstract
Traditional load-balancing techniques for multi-agent artificial intelligence (AI) systems are insufficient because they rely on infrastructure metrics rather than an agent's actual reasoning capacity. Proposed herein is a mechanism that addresses this gap by introducing an Available Cognitive Headroom (ACH) score, which enables predictive, capacity-aware routing that ensures agents are not cognitively overloaded before tasks are dispatched.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mohan R, Ram; Mahto, Annu Rani; Edavalath, Anis; Raj K, Aparna; and D, Sridhar, "SYSTEM AND METHOD FOR AVAILABLE COGNITIVE HEADROOM SCORING AND PREDICTIVE TASK ORCHESTRATION IN MULTI-AGENT AI SYSTEMS", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11207