Causal Guardrails for Artificial Intelligence

Large Language Models (LLMs) optimize next-token predictability and achieve remarkable fluency,yet exhibit persistent failure modes including hallucination, sycophancy, context drift, and behavioralinstability. These are often treated as defects of training, alignment, or evaluation. This paperadvances a structural diagnosis: such failures are expected consequences of finite agency operatingwithout explicit constraints on causal admissibility. We introduce Causal Guardrails, an architecturalframework that enforces pre-linguistic admissibility checks prior to language generation, based oninternal causal state, available agency, and invariant meaning constraints. We further connect inferencetime guardrails to learning-time constraints by integrating Causal-Access–Constrained Learning(CA–CL), which restricts optimization to information that is reliably accessible under deploymentconditions. We provide a rigorous adversarial stress analysis covering sycophancy traps, narrativejailbreaks, noise injection, deployment shift, and resource exhaustion, and propose causal compressionas the architectural patch for endurance without fabrication.