Plain-language queries
Ask the way you'd ask a colleague. It figures out the tables, joins, and filters: you don't touch SQL.
Executive tier · Analytics
An AI agent that answers questions about your data.
Ask in plain language ("which segments churned most last quarter, and why?") and it queries your data, builds the chart, and gives you the answer with the caveats. No SQL, no ticket to the analytics team, no three-day wait.
What does an AI data analysis agent do?
A data analysis agent lets anyone interrogate the company's data in plain language. You ask a question; it translates that into the right query against your warehouse or tools, runs it, builds a chart, and explains the answer, including what the data can and can't support. It's interactive and ad-hoc: pull, not push. Executive tier, built on Claude. For recurring, scheduled summaries instead, use the Reporting Agent. The two are complementary, one answers new questions, the other delivers the standing ones.
theagency47 · Updated June 2026 The spec sheet
How the Data Analysis Agent actually works.
| Job-to-be-done | Answer ad-hoc business questions over company data in plain language, with a chart and caveats |
| Tier | Executive (decision support) |
| Underlying model | Anthropic Claude Sonnet |
| Trigger | On-demand question (chat / Slack) |
| Inputs | Warehouse / tool access (read-only), schema + metric definitions, access rules |
| Tools available | Read-only SQL, chart rendering, data-source connectors, schema introspection |
| Autonomous decisions | Query construction, chart type, relevant segmentation, caveat surfacing |
| Escalation rules | Ambiguous question → asks a clarifier · Out-of-scope / restricted data → declines with reason · Low-confidence result → flags uncertainty |
| KPIs measured | Questions answered, clarification rate, query accuracy, analyst-time deflected, adoption |
| Eval suite | 22 test cases (query correctness, ambiguity handling, no-hallucinated-numbers). |
What it does
The work it takes off your team.
Charts on demand
Returns the right visualisation for the question, not a raw number you still have to plot.
Explains the answer
Gives the takeaway in words, with the caveats: what the data supports and where it's thin.
Asks when unsure
If a question is ambiguous, it asks a quick clarifier instead of guessing and misleading you.
Respects access
Honours your data-access rules and read-only boundaries, so self-serve doesn't mean exposure.
No invented numbers
Answers only from real query results: if it can't compute it, it says so rather than fabricating.
Integrations
Standard integration stack.
- Warehouse: BigQuery, Snowflake, Postgres, Redshift
- Tools: GA4, Stripe, HubSpot, product DBs
- Interface: Slack, web chat, embedded panel
- Semantic layer: dbt / metric definitions (optional)
- Access: read-only, role-aware
Custom integrations with proprietary systems are quoted as add-ons. Not sure if yours fits? Describe your stack and we'll confirm.
What it looks like in practice
A typical run.
- Throughout the day, Team members ask questions in Slack and get charts + answers in seconds.
- In meetings, Live "what about X segment?" questions answered on the spot instead of "we'll follow up".
- Before decisions, Leaders pressure-test assumptions against real data without queuing work for analysts.
- Recurring asks, Anything asked repeatedly gets promoted into a scheduled report.
FAQ
Questions about the Data Analysis Agent.
Is it safe to point at our database?
Yes, it runs read-only, respects role-based access rules, and can be scoped to specific schemas. It cannot write or delete. Query behaviour is auditable, and accuracy is covered by an eval suite.
Will it make up numbers?
No. It answers only from actual query results and flags uncertainty or missing data rather than fabricating. "No-hallucinated-numbers" is an explicit eval category.
How is this different from the Reporting Agent?
This is interactive (you ask anything, any time). The Reporting Agent is scheduled (the same summary every period). They target different needs and don't overlap, many teams deploy both.
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