Abstract
A system is proposed herein that introduces seven methods that make long-running multi-agent artificial intelligence (AI) systems reliable, governable, and self-improving. The methods enforce declared nondeterminism boundaries at compile time and runtime; preserve correctness during context compaction through contractual recovery testing with rollback; detect behavioral drift through adaptively calibrated monitoring; preserve coherence when decomposing large tasks through immutable skeleton contracts; maintain knowledge consistency through dependency-based memory revalidation; autonomously generate and safely deploy new agent capabilities through risk-tiered governance; and eliminate handoff fragility between AI models through a single schema that drives prompt generation, hermetic context construction, and deterministic output repair. Each method may be practiced independently; any subset may be combined.
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Recommended Citation
Powell, Brian Christopher and Siyavudeen, Faisal, "GOVERNED EXECUTION AND RELIABILITY METHODS FOR MULTI-AGENT ARTIFICIAL INTELLIGENCE (AI) SYSTEMS", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11969