Explainable Model Routing for Agentic Workflows

Current AI routing systems lack transparency about cost-quality tradeoffs: The paper identifies that existing agentic workflows optimize for performance and cost but don't explain their reasoning, making it impossible for developers to know whether a cheaper model was chosen because it was genuinely better suited for the task or simply because of budget constraints.

Explainability enables accountability in AI decision-making: Topaz demonstrates that making routing decisions interpretable—through natural language explanations of how skill-match scores were weighed against costs—allows developers to audit system logic and understand the rationale behind model assignments, rather than accepting them as "black boxes."

Skill-based profiling creates granular capability understanding: The framework synthesizes performance data across diverse benchmarks into detailed capability profiles, enabling more precise matching between task requirements and model strengths rather than relying on generic performance metrics.

Transparency supports meaningful human oversight: By providing clear traces of routing decisions, the system allows developers to iteratively tune the cost-quality tradeoff based on their actual priorities, rather than being locked into silent algorithmic choices that may not align with their values or use-case requirements.

Responsible AI requires auditability, not just optimization: The paper argues that responsible deployment of complex AI systems requires formal mechanisms to record and explain trade-offs, not just technical performance improvements—a principle applicable beyond routing to broader AI governance.