What Is Data Vault?

What Is Data Vault?

Data Vault is a data-warehouse modeling methodology that separates business keys, relationships, and historical attributes into hubs, links, and satellites. In AI reliability work, it matters because the Vault often becomes the audited upstream source for training data, RAG snapshots, evaluation datasets, and production-trace reporting. FutureAGI does not run Data Vault warehouses, but it can tie downstream Dataset, KnowledgeBase, and trace results back to the exact Vault-derived snapshot used in an evaluation.

Why Data Vault Matters in Production LLM and Agent Systems

LLM and agent systems are downstream of someone’s data warehouse, even if engineering teams pretend otherwise. The training set comes from somewhere; the evaluation golden set comes from somewhere; the traces and outcomes are eventually loaded back into a warehouse for analytics and compliance review. If that warehouse is a Data Vault, three things matter for AI teams: auditability is built in, change is cheap, and lineage from raw source through derived metric is queryable.

The pain when this is missing is familiar. A regulator asks “where did the training data for this model come from?” and the team has a folder of Parquet files with no source link. A compliance audit asks for the exact state of the customer table on the day the eval was computed; without time-versioned satellites, the answer is “we don’t know.” A platform team rebuilds a feature from a star schema and discovers the upstream join silently dropped 4% of rows.

Agentic systems amplify this. An agent that consults a knowledge base must answer to the same lineage requirements; if the KB was built from a Vault, that lineage is mechanical, and FutureAGI evaluations against it stay auditable.

How FutureAGI Handles Data Vault Inputs

FutureAGI does not implement Data Vault and we don’t model warehouses. The connection is real but indirect: FutureAGI’s Dataset and KnowledgeBase artifacts are versioned and content-addressed, so an evaluation run pinned to dataset@v17 can be rerun a year later against the same exact rows. When that dataset was generated by an ELT job from a Data Vault warehouse, the lineage extends: Vault satellite version to ELT run to FutureAGI Dataset version to eval result.

A concrete example: an enterprise team builds a customer-support agent on retrieved policy documents. The policy data lives in a Data Vault; a nightly ELT materializes the current snapshot into a KnowledgeBase consumed by the agent’s RAG layer. FutureAGI runs Faithfulness and ContextRelevance against production traces, with each result tagged with the KnowledgeBase version and trace fields such as llm.token_count.prompt. When a regulator asks “what policy revision was active when this answer was generated?”, the team walks the chain: trace span to KB version to ELT job to Vault satellite revision.

FutureAGI’s approach is to treat warehouse lineage as part of the evaluation contract, not as a BI-only concern. Unlike LangSmith-style trace review that can score outputs without enforcing a versioned warehouse snapshot, FutureAGI ties evaluator results to the exact dataset and knowledge base snapshot they ran against. If the score drops after a Vault refresh, the engineer can compare dataset@v17 with dataset@v18, inspect the traceAI-langchain span, and decide whether to alert, roll back the derived snapshot, or run a regression eval before the agent serves the new knowledge base.

How to Measure Data Vault AI Health

Treat the warehouse-to-eval link as a lineage problem; measure both ends:

• Dataset version and KnowledgeBase version in every eval run — make these queryable.
• ContextRelevance and Faithfulness evaluators tagged with the data version they ran against.
• Lineage gap signals: number of evaluations that cannot be tied to a specific Vault satellite revision.
• Refresh-to-eval lag: how long after a Vault load does the eval cohort actually see the new data.
• Source-system drift: row counts and column-set changes between Vault refreshes that may signal upstream schema drift.
• Dashboard split: eval-fail-rate-by-cohort before and after each Vault refresh.

from fi.evals import Faithfulness

eval = Faithfulness()
result = eval.evaluate(
    response="The refund window is 30 days.",
    context=["Policy v17: refunds within 30 days."],
)
print(result.score)

Common Mistakes

• Treating Data Vault and LLM applications as unrelated systems — the lineage from Vault to evaluation is the audit trail.
• Materializing the same Vault snapshot into different downstream marts with different filters and not pinning which one feeds the model.
• Skipping satellite history when training models, which silently leaks future-looking data into the training set.
• Letting the Vault refresh schedule outpace the eval refresh schedule, so dashboards and traces measure different states of the world.
• Confusing hub keys with surrogate IDs in downstream pipelines, breaking joins back to the source of truth.