Neuro-Information Systems and Synthetic AI Agents in High-Stakes Strategic Planning

High-stakes executive decision-making under extreme market volatility and cognitive pressure frequently suffers from information overload, bounded rationality, and neurophysiological stress responses that degrade strategic outcomes. Conventional Decision Support Systems (DSS) fail to capture real-time cognitive feedback, while autonomous Artificial Intelligence (AI) models often operate as opaque "black boxes" disconnected from executive neuro-cognitive dynamics. This study presents the Neuro-Information Systems and Synthetic AI Agent Architecture (NSAA) an integrated socio-technical framework combining multi-channel neurophysiological feedback (EEG, prefrontal fNIRS, and Galvanic Skin Response) with a multi-agent reinforcement learning simulation engine. Utilizing a mixed-methods computational and experimental design, we evaluated 48 executive decision-makers across 288 high-stakes strategic trials subjected to 100,000 synthetic market iterations modeling black-swan macroeconomic shocks, competitive counterstrategies, and resource constraints. The empirical findings demonstrate that real-time neuro-cognitive tracking coupled with synthetic agent counter-scenario modeling reduces executive decision latency by 34.2%, mitigates physiological stress spikes (cortisol/GSR surges) by 41.8%, and elevates strategic choice accuracy by 27.6% relative to traditional business intelligence toolsets. Furthermore, we establish an Enterprise Risk Management (ERM) governance protocol that embeds synthetic AI agent recommendations into executive decision workflows without inducing automation bias or compromising fiduciary oversight.