Optimization Moderate Pressure Makes AI Marketing Agents Riskiest: An OpenClaw Study of Optimization Pressure, Manipulation-Risk Signals, and Governance

Artificial intelligence (AI) marketing systems increasingly plan campaigns, generate messages, assess performance, and revise outputs with limited human input. In such multi-agent systems (MAS), commercial optimization pressure may produce ethical compliance drift: gradual movement from acceptable persuasion toward detector-defined manipulation-risk signals. We examine this problem in a controlled OpenClaw simulation of a three-role AI marketing team. The study varies optimization pressure across baseline, moderate-pressure, and high-pressure operating regimes while holding the model backend, prompt pool, agent roles, and monitoring process constant. Optimization pressure significantly affected detector-defined manipulation-risk scores, and the observed pattern was non-monotonic. The moderate-pressure condition produced the highest observed mean, but moderate and high pressure were not statistically distinguishable in the pairwise comparison. These results reflect detector-based risk indicators in simulated marketing-agent outputs, not direct evidence of consumer harm, deception, or real-world behavioral manipulation. The findings support pressure-aware auditing as a standards-informed monitoring practice consistent with IEEE value-sensitive design principles, while also identifying the need for human annotation, hybrid detectors, and real-user validation before stronger governance claims are made.