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Hybrid LLM Router for Local Agentic Systems

This technical engineering account describes a production-ready hybrid LLM routing architecture that routes prompts between local small models and cloud frontier APIs to balance latency, cost, and reliability. The router uses three signal vectors—constraint density, context pressure, and a lightweight "scout" classifier (a ~1B model running <50ms)—to decide when to run local inference versus cloud models. The author reports quantization benchmarking (q4_K_M vs q8_0/GGUF), finding q4_K_M suitable for routine tasks but brittle for structured tool-calling; recommends reserving q8_0 slices for tool calls. The implementation emphasizes asynchronous parallel evaluation (asyncio), type-safe validation (Pydantic) with ValidationError-driven graceful fallback to cloud, observability metrics (route distribution, local validation failure rate, CPST), and computational sovereignty benefits of maintaining a local baseline.