Decouple "search" from "reasoning" in LLM agents and you can cut search costs by up to 98% while keeping accuracy nearly intact 🔌 Title: Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents URL: 🔌 Overview DSG separates search-based grounding from the language model's reasoning. It runs as an independent gateway compatible with the Model Context Protocol (MCP), acting as a vendor-agnostic intermediary layer. ❓ Challenges Solved In production LLM agents, real-time search grounding is tightly coupled to the model provider. ・This makes systems hard to inspect, reconfigure, repurpose, or migrate ・Search can cause "Search-Induced Verbosity" that violates strict output requirements Bundling search with reasoning was a bottleneck for both flexibility and cost. 💡 Methodology & Proposed Approach It places grounding at the interface between search and generation, not inside the model, exposing previously model-embedded elements as controllable first-class features. ・Provider routing (choose and switch search providers) ・Source-aware context rendering ・Configurable fallback mechanisms ・Retrieval-depth management ・Both exact and semantic caching 📊 Experimental Results ・SimpleQA: 86.1% accuracy (vs 87.7% native search) while cutting search costs by 91% ・99.4% warm-cache hit rate with 68% latency reduction ・Production e-commerce: matched native-search accuracy while cutting search costs by over 98% ・But native search kept an edge on recency-sensitive FreshQA queries #LLMAgents# #Search#