Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation
We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows execute deterministically without further model invocation. This paradigm has antecedents in prior work on declarative pipeline optimization (DSPy) and hybrid neural-symbolic planning (LLM+P); our contribution is a systems-oriented study of its application to high-stakes enterprise workflows, with particular emphasis on healthcare settings where reliability and auditability are critical. By constraining generation to narrow business-logic functions embedded in validated templates, compiled AI trades runtime flexibility for predictability, aud...
| Authors | Geert Trooskens, Aaron Karlsberg, Anmol Sharma et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 April 2026 |
| Last updated at source | 31 July 2026 |
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