Do not connect a data source to an AI agent because it has a high completeness score or because every field passes a format check. Data quality is fit-for-purpose: a dataset is ready only when it contains the right information, for the right population and operating conditions, at the required level of accuracy, freshness, traceability and control for the agent’s job.
A defensible assessment therefore starts with the agent’s intended use and consequences, turns that use into measurable quality requirements, tests the source against realistic conditions, records lineage and limitations, and sets a gate for connection. The checks continue after launch because sources, populations and business rules change.
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What “good data” means for an AI agent
An AI agent can retrieve information, draft content, classify records, recommend actions or take actions through connected tools. Each use creates different data requirements. A customer-support agent may need current product entitlements and policy versions; an operations agent may need low-latency sensor readings; a research assistant may need broad coverage and citations; an automated approval agent may require highly accurate, auditable fields and mandatory human review.
That is why a dataset can be complete and internally consistent yet still be unsuitable. It may represent the wrong customers, stop before the period the agent operates in, omit rare but high-impact cases, or contain values that are consistently wrong. The relevant question is not “Is this data clean?” but “Is this data adequate for this agent’s defined task, users, risks and operating conditions?”
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There is no universal “AI-ready” percentage or pass score. A NIST AI Risk Management Framework request-for-information response, drawing on Richard Wang and Diane Strong’s 1996 data-quality work, argues that quality dimensions should be selected for the specific question an AI system must answer. Thresholds are consequently project decisions tied to impact and risk, not values that can be copied from a generic profiling report.
Start with the intended use and consequences
Write a short use statement before examining columns. It should be specific enough that another person can tell which records and fields are necessary and what happens when the agent is wrong.
Define the agent’s job
- State the tasks the agent may perform, such as answering questions, extracting facts, ranking cases, generating a recommendation or executing a transaction.
- List the information required for each task and distinguish required fields from optional context.
- Describe the expected operating conditions: geography, language, customer segment, time period, workload, latency and allowed tools.
- Specify which decisions remain with a person and which actions, if any, the agent may take without approval.
Map the consequences of error
Identify who could be affected by an incorrect, stale or missing value. A wrong product description may cause inconvenience; a wrong eligibility or safety value can cause material harm. The higher the consequence, the stronger the evidence, review and access controls should be before connection.
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Set knowledge boundaries
Document questions the agent must decline, escalate or answer only with a cited record. A source that is adequate for internal policy lookup may be inappropriate for making an individualized decision. These boundaries become part of the connection design, not an afterthought in the prompt.
Choose quality dimensions that fit the task
Use a written specification for the selected source. For every dimension, record how it will be measured, which fields or records are critical, the acceptable limit, the evidence required and what happens when the limit is missed. The following dimensions are common starting points; add others when the use case requires them.
| Dimension | What to examine | Why an agent may need it |
|---|---|---|
| Accuracy | Whether values agree with a trusted reference or known ground truth. | An agent can produce fluent answers from values that are systematically wrong. |
| Completeness | Presence of required fields, records and time periods; patterns of missingness. | Missing information can cause an answer, recommendation or action to be incomplete. |
| Consistency | Agreement between fields, records, systems and versions of the same entity. | Contradictory facts can make retrieval and reasoning unreliable. |
| Timeliness | Record age, update lag, event-to-ingestion delay and refresh regularity. | A correct value from last month may be unsafe for a real-time task. |
| Validity | Allowed values, data types, formats, ranges and business-rule constraints. | Invalid dates, units or codes can break tools or mislead the model. |
| Uniqueness | Duplicate entities, events or documents and the rules for deduplication. | Duplicates can inflate evidence, trigger repeated actions or create conflicting answers. |
| Representativeness | Coverage of the populations, conditions, languages and periods the agent will encounter. | Overall averages can hide poor performance for a small but important segment. |
| Relevance | Whether each field and record contributes to the defined task rather than adding distracting or prohibited context. | Irrelevant data increases retrieval noise and may create unnecessary privacy risk. |
| Traceability | Ability to identify the origin, transformation, version and responsible owner of a value. | People need to verify an answer, investigate an incident and explain a decision. |
| Precision and granularity | Level of detail, measurement resolution, units and rounding appropriate to the decision. | Rounded or aggregated data may be adequate for trends but not for an individual action. |
The UK Government Data and AI Ethics Framework names completeness, uniqueness, consistency, timeliness, validity and accuracy, and recommends fit-for-purpose data with ongoing validation. NATO’s Data Quality Framework for the Alliance, published August 29, 2025, describes dimensions as measurable attributes and highlights accuracy, completeness and consistency. These lists are useful prompts, not a mandatory universal test suite.
A practical pre-connection assessment
1. Turn the use case into a data contract
Create a one-page contract between the agent owner, data owner and risk or compliance owner. Include the task, permitted actions, affected groups, required fields, source systems, refresh expectation, human checkpoints and refusal conditions.
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2. Select dimensions and thresholds
Mark each dimension as critical, important or informational for this use. Then define a measurement and an acceptance rule. A critical field might require validation against a master system; an informational field might be monitored for trend only. Do not assign a percentage merely because a tool offers one. The acceptable limit depends on the consequence of error, the availability of human review and the agent’s ability to abstain.
Specify exception handling at the same time. A failed check may block the entire connection, quarantine affected records, narrow the agent’s permissions, require a human confirmation or trigger a source-owner remediation ticket. Without a pre-agreed response, a dashboard becomes a report rather than a control.
3. Profile the source and validate rules
Profile a representative extract, not just a small convenient sample. Record row and entity counts, field types, null patterns, distinct values, distributions, outliers, duplicate keys and update timestamps. Break results down by relevant segment, period and source system so that an acceptable overall average does not conceal a critical gap.
- Completeness: calculate missingness for each required field and identify whether missing values cluster by region, product, time period or workflow stage.
- Validity: test data types, allowed codes, date ranges, units, referential integrity and business rules. Keep rejected values for investigation rather than silently coercing them.
- Uniqueness: test the declared key and near-duplicate patterns. Decide which record wins when updates, merges or late-arriving events conflict.
- Consistency: compare equivalent values across systems and check relationships such as status versus closing date or currency versus amount.
- Timeliness: measure age at the moment the agent will read the data, ingestion delay, refresh failures and the distribution of update intervals.
- Accuracy: compare critical values with a trusted reference, reconciliation, labeled sample or other documented ground truth. State the method and its limitations.
These checks operationalize the dimensions described by NIST, the UK framework and NATO; those frameworks do not prescribe one fixed query set or tool.
4. Test realistic operating conditions
Build a test set that reflects the agent’s expected use rather than selecting only easy or frequent records. Include normal cases, edge cases, recent and older records, multilingual or regional variants where relevant, and the conditions under which the agent will be asked to act. Document how the set was sampled, the time window, exclusions, labels and evaluation method.
Assess results by meaningful segments when the data or risk warrants it. An overall accuracy or coverage figure can mask a failure for a small population. Look for sparse segments, missing periods, changed schemas and conditions that never appear in the source. If a segment is outside the evidence base, mark it as an operating boundary and require abstention or human handling there.
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5. Capture lineage, rights and limitations
Maintain an inventory for every connected source:
- Owner and technical contact responsible for corrections and availability.
- Collection or generation context, including the original system, process and intended purpose.
- Record and schema versions, timestamps, transformations, filters, joins, imputations and redactions.
- Access rights, retention rules, permitted uses and restrictions on personal or confidential information.
- Known biases, blind spots, sampling changes, unresolved defects and segments without adequate evidence.
- Checksums, run identifiers or equivalent references that let an investigator reproduce the data presented to the agent.
NIST’s AI risk guidance calls for documenting upstream data and software risks and connects provenance with transparency and accountability. Public-sector guidance likewise recommends recording known limitations, biases and quality concerns. Provenance is useful only when it is maintained alongside the data and exposed to the people responsible for review.
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Use a decision record rather than a single score. It should identify the assessed use, data version and test date; show each critical result; list unresolved defects and accepted exceptions; name the accountable approver; and define expiry or reassessment conditions.
- Connect: critical checks pass, operating boundaries are understood and oversight is in place.
- Connect with controls: the source is useful but uncertainty remains; restrict tools or fields, require citations or confirmation, and route exceptions to a person.
- Defer or reject: a critical requirement fails, provenance is unknown, rights are unclear, or the test population does not support the intended use.
A connection should be narrower than the source when only part of the data meets the contract. Do not expose unassessed fields simply because they are available in the same API, table or document store.
How to compare two candidate data sources
When several sources could support the same agent, compare them against the same use-specific contract. A larger or newer source is not automatically better.
| Comparison axis | Questions to answer | Evidence to request |
|---|---|---|
| Task suitability | Does the source contain the facts and context required for each defined task? | Field-to-task mapping, sample records and documented exclusions. |
| Coverage and representativeness | Are the relevant people, products, geographies, periods and operating conditions represented? | Segment counts, time coverage, sampling method and known gaps. |
| Accuracy and validity | Can critical values be checked against trusted references and rules? | Reconciliation results, validation rules, error samples and test method. |
| Freshness and consistency | How quickly does data change, and do equivalent values agree across systems? | Update-lag distribution, refresh history, conflict rates and version policy. |
| Lineage and accountability | Can an owner explain where values came from and how they changed? | Data catalog entry, transformation log, schema history and support contact. |
| Limitations and governance | Are bias, privacy, access, oversight and intended-use restrictions documented? | Risk assessment, rights documentation, limitation register and escalation process. |
Prefer the source that provides stronger evidence for the agent’s critical requirements, even if it has fewer records. If the sources complement each other, define conflict resolution before joining them; otherwise the agent may retrieve two plausible but incompatible answers.
Accuracy testing that stands up to scrutiny
Accuracy is not established by a clean schema or a plausible-looking sample. Use a reference appropriate to the field: an authoritative system, a controlled measurement, a documented reconciliation or a reviewed label. Explain how the reference itself was produced and where it may be uncertain.
Keep the evaluation method with the result. Record the sampling frame, sample size, labeling instructions, adjudication process, matching rules, time period and segment definitions. Separate errors caused by the source from errors caused by joins, transformations or synchronization. If the agent will consume transformed chunks, embeddings or summaries, test that representation too; a correct source can lose qualifiers during transformation.
For high-impact tasks, test the agent’s behavior when evidence is missing or contradictory. A safe system should surface uncertainty, cite the applicable record or stop for review rather than inventing a value. This behavior is part of readiness because data quality and agent controls interact.
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Ask who is absent from the source and when it was collected. Coverage can fail by geography, language, disability, device, income, product tier, season, crisis period or workflow channel. A historical source may accurately describe yesterday’s process while misrepresenting a redesigned process today.
Use segment-level counts and error analysis to find thin evidence. Do not infer that a small segment is safe because the overall average is strong. If the agent’s intended population is broader than the tested population, narrow the declared use, collect additional evidence or require human handling outside the tested boundary.
Bias documentation should be concrete: identify the affected segment, the suspected mechanism, the evidence available, the potential consequence and the mitigation owner. “The data may be biased” is not an actionable limitation unless people know how it changes deployment.
Provenance, privacy and accountability controls
Connecting data to an agent changes who can retrieve, combine and act on it. Confirm that the proposed use is permitted, that the agent receives only necessary fields, and that sensitive attributes are protected according to applicable policy. Keep access logs and make it possible to determine which source version supported an answer or action.
Assign ownership at three levels: the data owner for source quality and change notices, the agent owner for prompts, tools and operating boundaries, and the risk or business owner for acceptance of residual risk. A technical team should not silently accept a business-quality exception, and a business owner should not approve an unknown data lineage without technical evidence.
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Controls after the agent is connected
Readiness is a point-in-time decision; quality maintenance is continuous. The UK Government Data and AI Ethics Framework states, “Maintaining data quality is a continuous process.” NIST’s AI RMF Core similarly says, “Risk management should be continuous, timely, and performed throughout the AI system lifecycle dimensions.”
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Monitor the source
- Track schema changes, field missingness, validity failures, duplicates, update lag and volume by important segment.
- Compare distributions with the approved baseline and investigate material shifts rather than treating drift as automatic proof of failure.
- Alert on broken refreshes, unexpected deletions, new codes, unit changes and conflicting master values.
Monitor agent use and outcomes
- Sample answers or actions for factual support, citation accuracy, stale information and correct handling of uncertainty.
- Track escalations, overrides, corrections, user complaints and incidents by task and affected segment.
- Review whether users are asking the agent to perform a new task or access a broader population than the approved contract.
Define reassessment triggers
Reassess after a source-system migration, schema or business-rule change, major population shift, new data vendor or transformation, material drift, security or privacy incident, or expansion to a new task or geography. Set a periodic review as a backstop even when no alert fires.
When a check fails, pause the affected capability or reduce access first, preserve the evidence, notify the accountable owner, correct or quarantine the data, rerun the relevant tests and record the decision to restore service. Do not overwrite the prior assessment; the history is part of accountability.
Using the main governance frameworks without turning them into a scorecard
NIST AI Risk Management Framework
The NIST AI RMF is voluntary and organized around Govern, Map, Measure and Manage. Its Playbook, based on AI RMF 1.0 released January 26, 2023, describes itself as “a companion AI RMF playbook for voluntary use” and offers suggested actions, references and guidance for those four functions. Use it to assign responsibilities, understand context and impacts, measure evidence, and manage residual risk.
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UK Government Data and AI Ethics Framework
This framework provides a practical vocabulary—completeness, uniqueness, consistency, timeliness, validity and accuracy—and emphasizes fit-for-purpose data and ongoing validation. It is useful for turning a broad quality discussion into checks that a data owner can maintain.
NATO Data Quality Framework for the Alliance
Published August 29, 2025, NATO’s framework describes quality dimensions as measurable attributes of data assets and names accuracy, completeness and consistency. It can help teams define shared terms when multiple organizations exchange data, but it does not create a universal threshold for every agent.
A compact approval checklist
- The agent’s tasks, affected people, permitted actions and human decision points are written down.
- Required fields, units, keys, authoritative versions and refusal conditions are defined.
- Critical dimensions and use-specific acceptance limits have named owners.
- Completeness, validity, uniqueness, consistency, timeliness and accuracy checks have been run on a representative extract.
- Coverage has been tested across relevant populations, periods and operating conditions.
- Accuracy results include a documented reference, sampling method and segment analysis.
- Lineage, transformations, rights, retention and known limitations are recorded.
- Connection scope, access controls, citations, logging and human review are configured.
- Unresolved defects, exceptions and residual risk have an accountable approver.
- Monitoring, alert thresholds, reassessment triggers and rollback or quarantine steps are ready.
If any critical item is unknown, the defensible choice is to narrow the use, add controls or defer the connection—not to label the data “AI-ready” on the strength of a generic score.
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