The future of AI agents isn't a single frontier model. It's adaptive intelligence

Box describes an adaptive model-selection approach for enterprise AI agents designed to reduce the compounding costs of using frontier models across multi-step tasks without diminishing overall performance. Its recursive parent-child architecture assigns a parent agent to plan, coordinate, and synthesize work while isolated child agents handle focused subtasks such as retrieval, tool use, analysis, and workflow updates. Middleware evaluates runtime signals, including task stage, retrieval quality, failures, and contextual complexity, to choose lightweight models for routine operations and more capable models for consequential reasoning. In evaluations on roughly 400 enterprise tasks, adaptive Gemini execution improved task success from 71.1% to 74.6% while reducing cost by 21%, and adaptive Claude execution maintained a comparable 78.5% success rate while cutting cost by 25%. The findings suggest that matching model capability to individual execution steps can lower token consumption, limit unnecessary verbosity and tool-call errors, and preserve or modestly improve end-to-end agent performance.

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