Pipeline Denotational Design (PDD) is a design-first methodology for building data pipelines that are correct by construction. As AI agents generate pipeline code at scale, the bottleneck shifts from writing to verifying pipelines, and the errors that matter most (grain inconsistencies that silently inflate aggregates) evade schema checks, type checks, and sampled tests. PDD designs pipelines in a semantic domain rather than in code: a design is composed from a typed algebra of operations (the Pipeline Design Algebra, one instantiation) in which every well-typed composition is grain-correct by construction. Resting on grain alone, the guarantee is universal: it holds for any grain-inferring operation set, over any engine. Correctness is established in three layers (grain, behavioral class, and domain) at design time, at zero cost, with no access to data: grain by a data-independent computation (CalcG), behavioral class by the type checker, and domain rules by a proof-carrying composition over operation contracts. A Pipeline Correctness theorem collapses the rest to a single input-boundary check: the only data-dependent residue is whether inputs meet the design's preconditions (data quality, not code correctness), which PDD emits as SQL/PySpark verification queries. Correctness by construction is a spectrum: the same design can be verified by runtime checks, the deployed type-level checker, or machine-checked proofs in Agda/Lean 4. This recasts the engineer's role: an agent instantiates a pre-verified pattern and ships a machine-checkable certificate; the human validates a compact specification and checks it. We realize the methodology in a production toolchain and set out an evaluation across pipeline patterns, behavioral classes, and data modelling paradigms. Because it rests on grain alone, the same design-time check extends to AI-generated queries over semantic layers and ontologies.

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