Explainable Model Routing for Agentic Workflows
Summary: arXiv:2604.03527v1 Announce Type: new
Abstract: Modern agentic workflows decompose complex tasks into specialized subtasks and route them to diverse models to minimize cost without sacrificing quality. However, current routing architectures focus exclusively on performance optimization, leaving underlying trade-offs between model capability and cost unrecorded. Without clear rationale, developers cannot distinguish between intelligent efficiency — using specialized models for appropriate tasks — and latent failures caused by budget-driven model selection.
We present Topaz, a framework that introduces formal auditability to agentic routing. Topaz replaces silent model assignments with an inherently interpretable router that incorporates three components:
- Skill-based profiling: This component synthesizes performance across diverse benchmarks into granular capability profiles, allowing for a comprehensive understanding of each model’s strengths and weaknesses.
- Fully traceable routing algorithms: These algorithms utilize budget-based and multi-objective optimization to produce clear traces of how skill-match scores were weighed against costs, ensuring transparency in the decision-making process.
- Developer-facing explanations: By translating these traces into natural language, developers are empowered to audit system logic and iteratively tune the cost-quality tradeoff, enhancing the overall workflow.
By making routing decisions interpretable, Topaz enables users to understand, trust, and meaningfully steer routed agentic systems. The framework not only enhances the efficiency of task completion but also fosters a stronger relationship between developers and the models they employ.
In the evolving landscape of artificial intelligence, where decisions are often made by complex algorithms, the need for transparency and interpretability is more critical than ever. Topaz addresses this challenge by providing a structured approach to model routing that considers both performance and cost, without compromising on the quality of the output.
As organizations increasingly rely on AI to automate workflows, the implications of model selection and routing become paramount. The potential for budget-driven failures not only impacts efficiency but can also lead to significant setbacks if the wrong models are chosen for specific tasks. Topaz mitigates these risks through its innovative approach, ensuring that each model’s capabilities are aligned with the task requirements.
Furthermore, the introduction of skill-based profiling allows organizations to adapt their AI systems dynamically. As new models and techniques emerge, their performance can be assessed and integrated into the existing framework, ensuring that the routing remains optimal over time.
In conclusion, Topaz represents a significant step forward in the field of agentic workflows. By prioritizing explainability and auditability in model routing, it not only enhances operational efficiency but also builds trust in AI systems, paving the way for more robust and reliable applications in various industries.