The use of Artificial Intelligence (AI) in contract negotiation is stretching the limits of legal automation and raising important ethical and legal questions. The study evaluates five popular AI-driven contract platforms: LexAI, ClauseBot, LegalMind, JurisDraft, and SmartClause5, in five contract types (leasing, employment, procurement, IP licensing, NDAs). As part of a multi-dimensional approach, the research evaluates the performance of platforms in clause amendment, draft throughput, enforceability calibration, multi-party negotiation support and transparency. A series of refined equations and indices were developed to measure the operational and semantic reliability of each of the vehicles under negotiation stress. The results further demonstrate that systems with the more advanced natural language processing and adaptive semantic modeling technology, including LegalMind and LexAI, performed better than the rest in producing legally coherent and enforceable agreements in the shortest possible time frames. However, there were substantial differences in jurisdictional transferability, traceability, and bias sensitivity, highlighting ongoing shortcomings in existing AI frameworks. It’s yet another reminder of the need for human oversight and ethical control of automated legal decisions. The findings from this study suggest that although AI can produce significant efficiencies in contracting contexts, such technology should continue to be limited to hybrid model approaches involving machine-generated output and legal review. The results support the adoption of standard auditing procedures and indicate the relevance of aligning algorithmic power with de jure expectations. Future work is also needed to investigate enforcement outcome in the field, inter-cultural negotiation dynamics, and the development of AI legal agency.