At a glance Problem : Adapting large language models to specialized, high-stakes domains is slow, expensive, and hard to reproduce.  What we built : AutoAdapt automates planning, strategy selection (e.g., RAG vs. fine-tuning), and tuning under real deployment constraints.  How it works :  A structured configuration graph maps the full scope of the adaptation process, an agentic planner selects and sequences the right steps, and a budget-aware optimization loop (AutoRefine) refines the process within defined constraints.   Why it matters : The result is faster, automated, more reliable domain adaptation that turns weeks of manual iteration into repeatable pipelines.  Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be.…