RESEARCH
Technical research and engineering insights from building production AI agents — covering architecture patterns, cost optimization, caching strategies, and multi-agent systems.
A three-tier cache hierarchy — prompt templates, semantic response cache, and KV context window — adapted from CPU cache design principles to slash LLM inference costs by up to 90%.
How coordinated agent teams can save 30% over independent agents through shared context, redundant retrieval elimination, and optimized communication protocols.
A two-agent code generation loop — SDM (expensive model, minimal output) plans and reviews, SDE (cheap model) implements — achieving 80–90% cost reduction over single-model agentic coding.