What Is Data Integrity?

What Is Data Integrity?

Data integrity is the data reliability property that says evaluation rows, labels, references, metadata, and trace links remain accurate, complete, consistent, and provable from creation through reuse. In LLM and agent systems it appears in dataset construction, eval pipelines, production trace promotion, and training or fine-tuning gates. FutureAGI anchors data integrity in sdk:Dataset, where teams preserve row provenance, validate schemas, attach evaluators, and block releases when corrupted or stale examples would distort reliability scores.

Why Data Integrity Matters in Production LLM and Agent Systems

Data integrity failures create false confidence before they create obvious outages. A RAG system can pass release gates because the expected answer is stale, not because the retriever improved. A support agent can look accurate because duplicate rows overweight happy-path refunds while chargeback, privacy, and escalation rows are missing. A fine-tuning run can absorb labels that were never reviewed, then make the same bad behavior harder to detect later.

The pain lands differently by role. Developers lose reproducible failure cases because the row no longer matches the trace that created it. SREs see eval-fail-rate-by-cohort move after a deploy but cannot tell whether the product changed or the dataset changed. Compliance teams cannot prove who approved a risky label, which rubric version applied, or whether PII rows were redacted before reuse. Product teams see score swings that look like model improvement but are really reference drift.

Common symptoms include missing source_trace_id, null expected outputs, inconsistent label enums, duplicate prompts with conflicting references, sudden pass-rate jumps after dataset edits, and evaluator disagreement concentrated in one cohort. These problems get worse in 2026-era agent pipelines because one row may represent retrieval, planning, multiple tool calls, model fallback, and a final answer. If the row loses its provenance or schema, the team cannot know which step failed.

How FutureAGI Handles Data Integrity

FutureAGI’s approach is to make integrity a property of the eval dataset, not a spreadsheet cleanup step. The concrete surface is sdk:Dataset, exposed as fi.datasets.Dataset. Engineers create or import datasets, add typed columns and rows, import files or Hugging Face data, attach evaluations, inspect eval stats, and keep versioned evidence attached to the row that produced a score.

A realistic workflow starts when a production trace from a LangChain support agent is promoted into a regression dataset. The row includes input, expected_response, retrieved_context, source_trace_id, dataset_version, rubric_version, reviewer_status, and cohort. traceAI instrumentation can preserve span evidence such as agent.trajectory.step and llm.token_count.prompt, so the row links back to the retrieval, planning, and generation steps that made it worth testing.

FutureAGI then uses evaluator classes for targeted checks. FieldCompleteness catches missing required fields before a row enters a release gate. SchemaCompliance checks structured outputs against the expected schema. GroundTruthMatch compares the generated answer with the approved reference. If a new prompt raises the overall score but fails the “billing escalation” cohort, the engineer blocks the release, reviews changed rows, and reruns the regression eval after fixing references or prompt logic.

Unlike a Great Expectations table check that mostly validates warehouse constraints, this workflow connects data integrity to LLM behavior, traces, and evaluator outcomes. In our 2026 evals, the most useful integrity signal is not “all rows are present”; it is “every score can be explained by a trusted row, reference, rubric, and trace.”

How to Measure or Detect Data Integrity

Measure data integrity before using a dataset for release decisions:

• Missing-field rate: percent of rows missing required fields such as input, expected_response, source_trace_id, cohort, or dataset_version.
• FieldCompleteness result: checks whether structured records include the fields needed for reliable scoring.
• SchemaCompliance result: verifies structured outputs or expected responses match the declared schema before scoring.
• GroundTruthMatch disagreement: catches cases where a generated answer conflicts with the approved reference, often exposing stale ground truth.
• Provenance coverage: share of rows with reviewer, import source, trace link, rubric version, and dataset version populated.
• Dashboard signal: eval-fail-rate-by-cohort, duplicate-row rate, stale-reference rate, and user-feedback proxies such as escalation rate.

from fi.evals import FieldCompleteness, GroundTruthMatch

row = {"input": prompt, "expected_response": reference, "response": output}
for evaluator in [FieldCompleteness(), GroundTruthMatch()]:
    result = evaluator.evaluate(**row)
    print(result)

Common Mistakes

• Treating integrity as CSV validity. A parseable file can still contain stale labels, missing trace links, or conflicting references.
• Overwriting references without versioning. Silent edits break historical comparisons and make 2026 release gates hard to audit.
• Averaging across corrupted cohorts. One clean cohort can hide broken rows for locale, policy, tool path, or customer tier.
• Promoting production traces without review. Raw traces need redaction, rubric labels, and provenance before they become eval rows.
• Checking schema after scoring. Validate row shape before running evaluators, or bad rows will produce misleading pass rates.