We present Reflex Fabric, a local SQLite-based reflex layer that enables AI agents to complete high-frequency decisions in sub-millisecond time without invoking cloud LLMs. Operating as a sub-LLM layer analogous to the cerebellum in human motor control, the system handles routine decisions locally while reserving LLM capacity for genuine reasoning. Key innovations include a six-category reflex taxonomy, a strength decay model with configurable half-life, automatic nighttime consolidation, and a hardening mechanism for permanent reflex solidification. Benchmarks show 0.0034ms average lookup time—2.4 million times faster than typical LLM routing—while maintaining full offline operability when cloud services fail.
We present Reflex Fabric, a local SQLite-backed reflex layer that operates below the LLM inference layer in AI agent architectures. Inspired by the neuroscience distinction between cortical deliberation and cerebellar motor programs, Reflex Fabric enables sub-millisecond decision execution for high-frequency agent tasks without invoking cloud LLMs. The system classifies agent behaviors into six reflex types (R/I/E/C/M/P), maintains dynamic strength scores using strength = hits / (hits + misses + 1) with configurable half-life decay, and permanently hardens high-confidence patterns via a Long-Term Potentiation analog. Benchmark results show 0.0034ms average lookup latency — a 2,400,000x speedup over LLM-based routing — with full offline availability. The system requires only Python 3.8+ and SQLite with no external dependencies.
