Hierarchical cascade architecture of language models for multi-stage query classification and agent routing

Abstract

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models ('agents') responsive to a received query by using a hierarchical model cascade to classify queries into agent domains. Queries are processed iteratively by a series of hierarchical levels containing one or more AI models, where each layer is more complex and imposes fewer resource constraints. Each level generates a classification and a confidence score pertaining to the classification. A dynamic bypass mechanism analyzes the classifications and confidence scores at each level to dynamically determine if one or more levels of the hierarchy can be bypassed while resulting in an accurate classification. The final classifications are matched to one or more agents that process the query. Responses from the candidate agents are aggregated into an output that is responsive to the input.