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CLAP proposes a closed-loop method for domain agent post-training that converts noisy business data into structured SFT and preference samples, integrates reward/KL diagnosis, offline gates, and application-chain replay to decide adapter release. Experiments on five manufacturing batches show modest average gains and highlight that regression and high KL risks require an integrated data-training-evaluation-release loop rather than relying on a single score.