The Intelligence Stack: Engineering Production-Grade Agentic AI Systems

Building production-grade agentic AI systems requires solving cost and reliability engineering challenges beyond just prompting. The article covers a full stack approach including intelligent request routing via lightweight classifiers, model distillation to compress frontier model capabilities into smaller deployable models, parameter-efficient fine-tuning with QLoRA/LoRA adapters, hybrid RAG with data taxonomy awareness, and efficient serving through vLLM with continuous batching and PagedAttention. It also addresses when to use reasoning-class models like o3 and DeepSeek R1, emphasizing that the cheapest token is the one never generated.