Agentic Compilation: Solving the LLM Rerun Crisis for Zero Inference Cost Web Automation

The proliferation of Large Language Models (LLMs) has catalyzed the development of autonomous web agents capable of navigating complex user interfaces. However, the prevailing paradigm—Continuous-Loop Action generation (e.g., Think-Act-Observe)—suffers from severe inference latency, context window degradation, and prohibitive token costs at scale. In this paper, we identify the "Rerun Crisis" facing modern agentic automation and propose a highly optimized "Compile-and-Execute" implementation that fundamentally decouples LLM reasoning from physical browser execution. We introduce a DOM Sanitization Module (DSM) that compresses web page topology into a token-efficient semantic skeleton. A single LLM inference call acts as an autonomous planner, compiling this skeleton and user intent into a deterministic JSON workflow blueprint. We present a cost-evaluation framework demonstrating that continuous agents scale in inference cost at O(M × N) per rerun, whereas our One-Shot Compiler scales at $O(1)$. Execution is subsequently offloaded to a local engine, eliminating continuous AI inference overhead. By integrating a Human-in-the-Loop (HITL) gate for Supervised Autonomy and predictable failure halting for dynamic UIs, this architecture provides a highly scalable, safe, and cost-bounded framework for the next generation of enterprise AI automation.