Genomic surveillance can reveal pathogen diversity at a resolution that conventional typing cannot provide, yet sequence resolution alone does not determine what veterinary services should investigate, escalate, control, or communicate. This article develops an original non-empirical decision architecture for converting genomic observations into bounded veterinary surveillance evidence. The analysis separates sampling design and representativeness from sequencing and assembly quality; distinguishes variant or lineage detection from transmission inference; and treats antimicrobial-resistance, virulence, and host-adaptation findings as signal classes whose action relevance depends on analytical validity, phenotype, epidemiological context, and consequences of error. The proposed architecture positions genomic quality control as an upstream interpretation boundary and treats actionability as a revisable confidence state rather than an intrinsic property of a sequence. It further requires contextual reconciliation of genomic findings with host, place, time, movement, exposure, clinical or production phenotype, and surveillance-system constraints before risk escalation or targeted response. This structure is intended to make uncertainty operational: incomplete sampling, low genetic diversity, recombination, unusual mutation dynamics, genotype–phenotype discordance, pipeline changes, and missing metadata can prevent apparently precise genomic outputs from supporting precise decisions. The architecture does not provide universal distance thresholds, validated escalation rules, or proof that genomics improves veterinary outcomes. Its principal contribution is a testable way to separate description, evidence interpretation, decision confidence, and response selection so that genomic surveillance can be evaluated by whether it supports proportionate, traceable, and revisable action.
