Modern Al applications face rising operational costs, inefficient model utilization, context-window limitations, and the risk of hallucinated outputs. Existing systems often apply the same reasoning strategy to every query, resulting in unnecessary expenses and inconsistent response quality. We propose an Adaptive Multi-Modal Reasoning Engine (AMRE) that dynamically analyzes task complexity, routes requests to the most appropriate Al model, optimizes context through intelligent token compression, and verifies generated responses using a Hallucination Firewall. The system provides transparent routing decisions, measurable cost savings, and trustworthy Al outputs through an explainable and observable pipeline.
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