This workflow automates intelligent routing of user queries to optimal AI models (Anthropic, OpenAI) based on complexity analysis, then validates outputs through multi-stage quality assessment. Designed for teams managing high-volume AI operations, it solves the critical problem of balancing cost-efficiency with output quality—automatically selecting budget-friendly models for simple tasks while routing complex requests to premium models. The system analyzes incoming queries via validation tools, routes them through specialized AI agents based on assessment scores, executes parallel quality checks across compliance, bias, and risk dimensions, aggregates validation results, and stores flagged responses for human review. This ensures consistent, high-quality AI responses while optimizing computational costs and maintaining governance standards across diverse use cases.
Active API accounts for Anthropic Claude and OpenAI.
Customer support ticket routing and quality monitoring.
Adjust classification logic by modifying validation node expressions.
Reduces AI costs by 40-60% through intelligent model selection.