AI Behavioral Engineering
An AI engineering practice focused on the gap between AI that impresses in demos and AI that works correctly at scale.
AI agents fail in predictable, systematic ways. We build enforcement architectures that prevent these failures structurally: quality convergence, cognitive load management, and evidence-first protocols that eliminate speculation-driven development.
Standard AI tools treat memory as flat storage. We apply computational models of human cognition: biologically-inspired memory with spaced-repetition consolidation, reflexive intelligence, and emotional markers that shape how agents learn and recall.
Traditional testing can't find what it's not looking for. We use evolutionary algorithms to stress-test rule-based systems, discovering evasion vectors through population-based evolution and LLM-guided strategy synthesis in sandboxed isolates.