The intersection of artificial intelligence and complex reasoning tasks demands systems that can integrate diverse expertise while maintaining rigorous quality standards. This paper introduces a novel framework for coordinated multi-agent reasoning that addresses three fundamental challenges: maintaining analytical consistency across specialized agents, dynamically validating output quality without human intervention, and optimizing resource allocation across reasoning tasks. While demonstrated in the legal domain—specifically through a multi-agent system for patent infringement analysis—the framework's principles are domain-agnostic. The key theoretical contributions include a formal model of context-chaining that enables agents to build upon prior analyses while preserving specialized perspectives, and a multi-dimensional quality metric comprising coherence, groundedness, completeness, and structure that achieves 0.84 correlation with human expert judgments. Empirical evaluation shows that persona-based specialization yields 23% higher quality scores than single-agent baselines, context chaining contributes an additional 11% improvement, and iterative validation boosts reliability by 31%. From these results we derive three generalizable design principles for multi-agent reasoning systems: the specialization-consistency trade-off, the quality-cost frontier, and validation-driven optimization. These principles provide a blueprint for building AI systems capable of expert-level analysis across domains such as medicine, finance, and engineering.