In interactive text-to-SQL, the evidence needed to write correct SQL is not given upfront but distributed across schema, knowledge bases, and users, and must be acquired through costly environment interactions under a finite budget. We observe that strong LLMs often fail not from deficient SQL synthesis, but from premature commitment to SQL before acquiring task-critical evidence. Through controlled trajectory analysis, we formalize this observation as the Information Sufficiency Hypothesis and identify a five-class gap taxonomy that structures the acquisition space. Guided by this diagnosis, we develop PASCAL, a training-free acquisition-control data agent framework that interposes a source-aware information-acquisition layer before SQL generation and schedules acquisition by cost-normalized priority. On BIRD-Interact, PASCAL with a local 35B backbone achieves 51.0% Phase-1 and 37.0% two-phase success rate, outperforming frontier models leaderboard result by up to 14.7 percentage points, with gains concentrated on BI tasks requiring cross-source evidence acquisition.