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This paper introduces a benchmark for evaluating LLM agents as forward-deployed engineers in post-training delivery, highlighting the critical 'trains but does not learn' failure mode where models optimize without actual learning.
The article outlines a three-rung decision ladder for selecting between prompt improvements, retrieval-augmented generation, or fine-tuning in AI client projects, emphasizing the critical role of evaluation sets.
Drawing on the FDE roadshow experience, the article explores that what enterprises truly lack in AI implementation is not the model itself, but Forward Deployed Engineers (FDEs) who can connect models, business, data, and organizational responsibilities. It elaborates on their responsibilities, organizational mechanisms, technical and sales attributes, and the role of scientific evidence methods in delivery, proposing the view that one FDE leading a group of Agents can complete AI transformation delivery.