Agentic Compilation: Mitigating the LLM Rerun Crisis for Minimized Inference Cost Web Automation

Autonomous web agents that operate through continuous inference loops—repeatedly querying a large language model (LLM) to evaluate browser state and select the next action—exhibit a fundamental scalability constraint when applied to repetitive, high-volume tasks. We characterize this constraint as the Rerun Crisis: the linear growth of token expenditure and API latency with respect to both task length and execution frequency. For a representative five-step data extraction workflow executed across 500 iterations, an unoptimized continuous agent incurs approximately $150.00 in inference costs; even with aggressive state-caching, this figure remains near $15.00. We propose a Compile-and-Execute architecture that decouples LLM reasoning from browser execution, reducing inference expenditure for the same workload to under $0.10. A one-shot LLM invocation processes a token-efficient semantic representation of the target page—produced by a DOM Sanitization Module (DSM)—and emits a deterministic JSON workflow blueprint. A lightweight local execution engine then drives the browser without further model queries. We formalize the cost asymmetry as a reduction from O(M × N) to $O(1)$ inference scaling, where M is the number of reruns and N is the number of sequential actions per run. Empirical evaluation across three enterprise task modalities—high-volume data extraction, dynamic form filling, and technology-stack fingerprinting—yields compilation success rates of 80–94% and execution accuracies of 95–98%, at per-compilation inference costs between $0.002 and $0.092 across five frontier models. These results establish deterministic compilation as an economically viable and technically sound paradigm for high-throughput web automation.