Business meaning
Anchor questions in the organization's approved definitions.
- Metric and dimension mapping
- Ambiguity clarification
- Date and population context
Governed data exploration
Analytics AI agent with inspectable answers.
Translate business questions into permitted analytical queries, explain the results and help users inspect the data behind an answer.
- Permitted actions
- Read approved analytical data and metric definitions, run constrained queries and produce supported charts or summaries.
- Human approval
- Business owners approve metric definitions; users review ambiguous questions and consequential decisions based on the analysis.
- When work cannot continue
- Ask for clarification or return a data limitation when definitions, permissions or source coverage do not support a reliable answer.
Workflow design
How the analytics AI agent works
A reference workflow for the supported task. The specific tools, approval rules and operating limits are agreed during discovery.
- 01
Clarify the question
Resolve the metric, population, date range and comparison before querying business data.
Output
A question mapped to approved definitions
- 02
Construct a permitted query
Use governed models and enforce row access, read-only execution and resource limits outside the model.
Output
A validated query within the user scope
- 03
Execute and inspect
Run the query, check result shape and freshness, and retain the calculation and filter context.
Output
A result set with inspectable query context
- 04
Explain and follow up
Present an appropriate chart or summary with caveats; clarify follow-up questions without changing definitions silently.
Output
A supported answer and an evidence trail
Controls across the workflow
- Enforce permissions outside the model
- Validate tool inputs and results
- Limit retries and retain task state
- Log decisions with agreed data retention
Decisions that shape the scope
- Can the agent invent a metric?
- It can propose a definition for review, but should not present a new calculation as an approved business KPI. Existing semantic definitions take precedence.
- How is query access constrained?
- Apply permissions, allowed data objects and execution budgets in the query layer. Generated SQL is an input to validate, not a trusted instruction.
- What does a chart prove?
- A chart describes the selected data and calculation. It does not establish causation or prediction quality; those require separate analytical work.
Evidence before expansion
Define what better means.
These are proposed evaluation measures, not reported client results. Agree the baseline, sample and acceptance threshold before the pilot, then review the evidence with the workflow owner.
- Analytical correctness
- Compare queries, filters, aggregations and final values against approved answers for representative business questions.
- Access and resource control
- Test unauthorized data requests, expensive queries and malformed inputs against the intended execution limits.
- Answer inspectability
- Check whether users can identify the source, metric definition, time window, filters and limitations behind each answer.
Before you commit
Is this the right engagement?
Translate business questions into permitted analytical queries, explain the results and help users inspect the data behind an answer. A first release should cover a named task with representative examples and an accountable owner.
- What we need from you
- Bring common business questions, approved datasets, metric definitions, access policies, known correct queries and expected result examples.
- How you accept the work
- Agree representative cases, failure scenarios and acceptance thresholds with the workflow owner. Review analytical correctness, access and resource control, answer inspectability before expanding access or supported tasks.
- Scope & alternatives
- This is an interactive analytical agent. Recurring report production belongs to automated reporting; predictive models and business forecasting need their own validation and delivery scope.
Capabilities
What goes into your analytics AI agent
Implementation components are selected for the task and confirmed in scope.
Governed execution
Separate generated queries from permission and resource enforcement.
- Read-only tools
- Row-level access
- Query budgets and timeouts
Evidence-led presentation
Make answers useful without concealing the calculation.
- Query and source context
- Appropriate charts
- Freshness and limitation notes
Frequently asked questions
Analytics AI agent questions
01What is Analytics AI Agent designed to do?
What is Analytics AI Agent designed to do?
Analytics AI Agent is designed to provide a governed conversational layer over business data so users can ask questions, inspect charts or summaries, follow up on results, and trace the analysis back to approved data and metric definitions.
02Can Analytics AI Agent connect to our current data stack?
Can Analytics AI Agent connect to our current data stack?
Yes. It can be integrated with warehouses, lakehouses, semantic layers, BI environments, CRM or ERP data, and approved APIs, depending on how the organization governs analytical access.
03How does it avoid redefining business metrics?
How does it avoid redefining business metrics?
We connect the workflow to governed semantic models, metric definitions, documented transformations, or approved analytical data products where available, and expose the query or source context for review.
04Can Analytics AI Agent make predictions?
Can Analytics AI Agent make predictions?
It can support predictive or diagnostic methods when the data and use case justify them. Prediction quality is measured for the specific task and should be validated before business decisions depend on it.
05Can users customize dashboards and reports?
Can users customize dashboards and reports?
Yes. Views, KPIs, filters, visualizations, and conversational workflows can be designed around different roles while preserving shared metric definitions and access rules.
06Who is responsible for decisions based on Analytics AI Agent outputs?
Who is responsible for decisions based on Analytics AI Agent outputs?
The system can support analysis and recommendations, but people remain responsible for final business decisions. Higher-impact decisions should have proportionate review and governance.
Start a conversation
Scope your analytics AI agent
Share the task, systems and examples of a successful result. We can define the first workflow, required controls and evaluation plan.