# Conclusion

> **Status: SCAFFOLD.** Write last, ~1 paragraph. Restate the contribution and the
> headline result without hedging once numbers exist.

We presented a rigorous evaluation of a two-agent LLM workflow — a curator and a
harmonizer — for automating the harmonization of heterogeneous environmental
(soil-moisture) datasets into an analysis-ready schema, benchmarked against expert
ground truth via retrospective grouped cross-validation and prospective blind
evaluation. By scoring output-data equivalence (executed code, compared cell-by-cell
against the expert's), decomposing performance across the two agents, and attributing
end-to-end failures with an explicit taxonomy, the study `[[PLACEHOLDER: states what
was learned — the achievable accuracy, where error originates, and the operating
point at which the system is deployable with human oversight]]`.

`[[PLACEHOLDER closing sentence on broader significance: a path toward scalable,
auditable, FAIR-aligned curation of long-tail environmental data, with the agent
producing human-reviewable transformation code and documented mappings rather than
opaque outputs.]]`