What question did this study set out to answer?

This research aims to develop a safe execution framework for autonomous AI agents based on hardware safety principles.

May 29, 2026Open Access

MA Runtime: Applying Hardware Safety Topology to Autonomous AI Agent Execution

Puntos clave

This research aims to develop a safe execution framework for autonomous AI agents based on hardware safety principles.
Evaluated MA Runtime framework across N=151 sessions
Implemented safety architecture derived from emergency off circuit topology and cascade control theory
Analyzed task completion and legal interlock activation rates during execution
Achieved 84.8% task completion rate
Recorded 3.3% legal interlock activation during execution
Demonstrated 81% retry recovery rate after failures

Resumen

MA Runtime is an autonomous AI agent execution framework whose safety architecture is derived from hardware safety engineering — specifically Emergency Off (EMO) circuit topology, C-contact fail-over switching, and cascade control theory. The framework encodes structural safety invariants (C-contact fail-over, cascade delta gate) and a semantic legal interlock layer. Empirical evaluation across N=151 sessions demonstrates 84.8% task completion, 3.3% legal interlock activation, and 81% retry recovery rate. We also introduce the Contradiction-Induced Idea Emergence (CIIE) framework as a secondary contribution.

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Cite This Study

Daisuke Tsunemori (Thu,) studied this question.

synapsesocial.com/papers/6a192eb9fab5b468c4417e8b https://doi.org/https://doi.org/10.5281/zenodo.20415646

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