Key Evaluation Criteria for Data‑Driven AI Agents

Why Evaluate Data Agents

Data‑centric agents must turn nondeterministic, opaque, and error‑amplifying behavior into publishable engineering quality, which requires continuous convergence rather than ad‑hoc case checks. Compared with traditional software, agents face three inherent hurdles: nondeterministic outputs, black‑box decision processes, and error cascade amplification.

Methodological Highlights

Because the industry lacks a unified standard for agent evaluation, the process must be adapted to the current environment and refined through repeated practice. Core evaluation concerns include defining standards, building dataset assets, layered scoring, root‑cause attribution, structured optimization, and full‑loop feedback.

Stricter Requirements for Data Agents

Data agents—covering analytics, NL2SQL/NL2Data, data retrieval, reporting, and insight generation—output numeric results or conclusions, which demand zero tolerance for errors. A single misplaced JOIN or misinterpreted metric can produce a seemingly reasonable but completely wrong figure.

Therefore the evaluation focus shifts from pure “quality” to “data correctness + metric consistency + reproducibility”.

Recommended Evaluation Dimensions

Result Correctness (P0)

Execution success rate: SQL/queries must run without syntax, permission, or timeout errors.

Result consistency: Repeated generations of the same query should yield stable results.

Value matching: Compare each numeric answer line‑by‑line with the ground truth; allow floating‑point tolerance for aggregates but require exact matches for detail rows.

Schema alignment: Ensure the correct tables and columns are selected, with no hallucinated fields.

Semantic and Metric Correctness

Metric definition: Verify that formulas for GMV, active users, refund rate, etc., follow business definitions.

Time/dimension handling: Check correct interpretation of periods (last month, last 7 days, YoY) and grouping dimensions.

JOIN/filter logic: Confirm join keys, required WHERE clauses (e.g., partition or status filters), and absence of over‑ or under‑counting.

Process Metrics – Query Quality

Logical equivalence: Generated SQL should be semantically equivalent to the reference (via result comparison or SQL diff).

Partition pruning / scan volume: Detect full‑table scans versus partition‑pruned queries, as they impact cost and latency.

Dangerous operation interception: Guard against generated DROP/DELETE statements or unrestricted full‑table pulls.

Data Security and Compliance (P0 Gate)

Permission boundaries: Queries must only access tables/rows the user is authorized to read.

Sensitive field leakage: Ensure no exposure of masked data such as phone numbers, IDs, or salaries.

Data‑out‑of‑domain: Results must comply with the organization’s data‑security policies.

Clarification and Refusal

Ambiguous metric clarification: When a metric definition is unclear (e.g., “sales” inclusive or exclusive of tax), the agent should ask for clarification instead of guessing.

Inability recognition: If data is missing or permissions are insufficient, the agent must explicitly state the limitation rather than fabricating numbers.

Explainability and Product Quality

Agents should transparently disclose how a figure was derived—listing tables, time windows, and filters—mirroring the query‑quality checks above.

In summary, while conversational agents are evaluated on “right or appropriate,” data agents require additional scrutiny of numeric accuracy, metric fidelity, permission compliance, and silent‑error handling.