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What makes an AI data analyst reliable?
A useful analyst agent does more than produce plausible explanations. It must answer a defined business question using the right data, apply the organization’s metric definitions, inspect the results, and communicate conclusions that the evidence supports. People also need a way to correct its direction and reviewers need enough visibility to understand how it reached an answer.
These requirements are not tied to one model or platform. For example, OpenAI’s September 10, 2026 announcement describes its ChatGPT Work Data agent connecting to approved sources and using business terms, metric definitions, custom calculations, and data relationships to interpret data. That product example illustrates the components; it does not establish that every agent framework supports the same connectors or capabilities.
1. Define the questions the agent is allowed to answer
Begin with a bounded task, such as investigating a change in a defined metric, comparing customer segments, or explaining a trend over a specified period. “Answer any business question” is not a useful first scope: the agent cannot reliably interpret data or terms the organization has not made available and defined.
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For each task, specify what data is in scope and what a useful answer must include. A metric investigation might require the time period, comparison baseline, segments examined, evidence for the main finding, and any material limitations. Gather representative questions from stakeholders; those questions will later form part of the evaluation set.
- Name the business question and the intended user.
- Identify relevant sources, metrics, time periods, and permitted levels of detail.
- Define the expected answer shape—for example, a concise finding with supporting figures and the query or analysis used.
- List ambiguities that should prompt a clarification or an explicit assumption rather than an unsupported guess.
2. Connect approved data and teach the agent what it means
Give the workflow access only to data sources approved for the intended use. Available connections depend on the product and the organization’s setup. OpenAI’s September 10, 2026 announcement lists Amazon Redshift, Datadog, Google BigQuery, ClickHouse, Databricks, MongoDB, and Snowflake among sources for its ChatGPT Work Data agent, alongside files and documents from Google Drive and SharePoint. This is an example of that product’s stated integrations, not a universal connector list.
Connection alone is not context. Document the business vocabulary that translates raw schemas into analytical meaning: canonical metric definitions, calculation rules, relationships between tables or entities, and relevant caveats. If “active customer” has a specific organizational definition, supply it rather than expecting the agent to infer it from a column name.
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Permissions and governance belong in the design. Confirm which sources and records the agent may use and who may access its outputs. The product and evaluation sources cited here do not prescribe a universal security configuration, so apply the controls required by your organization and the data involved.
3. Make analysis an inspectable workflow
Structure the agent’s work as a sequence: interpret the request, choose an appropriate tool, retrieve or query data, inspect the returned result, and produce an explanation grounded in that result. The specific tools may vary, but the workflow should make consequential steps reviewable. Useful artifacts include the query or operation performed, the result returned, and the assumptions needed to interpret it—not a claim that every internal model thought is visible or correct.
For ambiguous questions, the agent should ask for clarification or state a bounded assumption before proceeding. Give users a practical way to redirect a mistaken path while the analysis is underway. OpenAI’s account of its in-house data agent describes users interrupting and redirecting analysis mid-run; that is a documented design example, not a guarantee across platforms.
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When assessing a platform or implementation, check whether reviewers can inspect traces and intermediate tool activity. OpenAI’s workflow guidance describes using traces to investigate agent behavior and graders to score criteria. Visibility helps diagnose failures; it does not by itself prove an answer is correct.
4. Test answers against expected analytical results
Build a test set from real stakeholder questions and give each case an expected result. Where it is useful, include a manually authored reference query or analytical operation. OpenAI’s account of its in-house data agent describes curated question-and-answer pairs and “golden” SQL, with generated SQL executed and both the SQL and returned data compared. Its approach allows materially correct results even when the generated SQL is not textually identical to the reference.
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- Include representative questions for every task in scope.
- Cover edge cases such as missing values, unusual time ranges, and relevant segment boundaries.
- Compare returned data with expected outcomes using criteria appropriate to the metric and task.
- Record known failure patterns and add user-reported mistakes as new test cases.
5. Evaluate the process and the final report
Measure more than final-answer correctness. A workflow can fail because it picked the wrong tool, supplied an unsuitable input, misread a tool result, or left the task incomplete—even if the final response happens to sound reasonable. Microsoft distinguishes system evaluation of the end result from process evaluation of the steps that produce it, including tool selection, input accuracy, and use of tool outputs.
Assess the report itself for relevance, completeness, and grounding in the available evidence. Microsoft’s documented evaluation dimensions include task completion, tool-call accuracy, tool selection, input accuracy, tool-output use, relevance, abstention, and answer completeness; where context is supplied, they also include groundedness and context coverage. Choose measures that match the failure risks of your own task rather than treating every dimension as equally important.
OpenAI’s workflow guidance describes a repeatable pattern: inspect traces, apply graders to defined criteria, organize cases in datasets, and rerun evaluations to compare behavior. Microsoft’s Agent evaluation overview puts the reason plainly: “Without evaluation, you can’t reliably measure whether changes to your agent improve or degrade quality.”
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6. Use evaluation as part of an ongoing release loop
Run the representative evaluation set after material changes to prompts, models, data connections, or schemas. Review failures, determine whether the cause is missing context, a faulty query, a tool-use error, or a reporting problem, and add important cases to the regression set. A repeatable test suite helps show whether a change improved or degraded measured behavior on those cases; it does not establish universal accuracy on questions the tests do not cover.
Evaluation is one quality practice, not a substitute for deployment review. Microsoft’s overview says it complements responsible-AI review, moderation, security testing, user research, and performance testing. Keep those checks in the release process according to the risks and controls relevant to your deployment.
How to choose an implementation
There is no universal best platform established by these product and evaluation examples. Compare candidates against the data stack, controls, and evaluation requirements you actually have:
- Can it reach the approved sources and files needed for the scoped questions?
- Can your team provide canonical terms, metric definitions, calculations, and data relationships?
- Can users correct or redirect analysis, and can reviewers inspect useful traces and tool activity?
- Can you evaluate both final task success and process quality against expected results?
- Can you rerun those evaluations after meaningful changes, and what separate security, privacy, safety, and performance checks are required?
Pick a workflow that fits those needs and prove it on representative questions before expanding its scope. The cited product pages describe distinct approaches; they do not provide a comparative performance ranking.
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