What is agentic business intelligence?
By SchemaChat (Data BI LLC) · Published 30 October 2026 · Last reviewed 30 October 2026 · 7 min read
Agentic business intelligence (agentic BI) is BI in which AI agents do the steps between a business question and an insight: deciding what to look at, running the queries, checking the results and writing up what they found. You give it a goal, not a query. It works best with a human reviewing the plan and the numbers.
“Agentic BI” is a new label, so it is used loosely. This page gives a plain definition, shows what an agentic analysis actually does, and is candid about where it can go wrong. We build an agentic BI product (SchemaChat), so we say where our view is ours and where it comes from other sources.
A plain definition
Databricks describes agentic BI as “a next-phase evolution in business intelligence that uses autonomous AI agents” to automate the steps between raw data and insight, in contrast to static dashboards that show pre-built reports. In everyday terms: a dashboard answers the question someone decided to track in advance. An agent starts from a goal — “why did margin fall last quarter?” — and works out which questions to ask.
How BI got here
- Reports: someone writes a query and sends a document.
- Dashboards: the same numbers, kept up to date, for questions you anticipated.
- Self-service: business users filter and slice within a model a data team built.
- Natural-language questions: you type a question and get an answer or a chart.
- Agentic: you state a goal; the system plans several questions, runs them, checks them and summarises.
What an agentic analysis does
Take the goal “Why did margin fall last quarter?”. An agentic system typically:
- Proposes a plan — for example: margin by month; margin by product; returns by product; discount rate over time.
- Runs each step as a query against your data.
- Checks each result: does it answer the sub-question, is the scope right, are the numbers in the write-up really in the data?
- Writes a summary that cites the step behind each figure.
- Says what it could not cover.
In SchemaChat this is the Analyze mode: the plan is editable before it runs, every step shows its SQL, small comparison groups are flagged (“Read with care”), and the summary ends with a “Not covered” line.
What agentic BI is not
- Not unsupervised. Databricks' own framing is that it does not replace BI teams: it delegates routine tasks so analysts can focus on higher-order work, while keeping oversight of what the agents produce.
- Not a chatbot answering from memory. A trustworthy system runs a query on your data, or says it cannot.
- Not forecasting or decision-making. It reads data you already have.
The governance question: semantic layer first, or schema first?
There are two broad approaches, and they trade effort for speed.
| Semantic layer first | Schema first, with visible checks | |
|---|---|---|
| How it works | A data team defines governed metrics and access; agents query those definitions | The agent reads your database schema; you can add governed metrics later |
| Strength | Consistent definitions; tighter control over what agents can ask | Starts in minutes; no modelling project |
| Cost | Modelling effort before the first answer | More reliance on checks and human review of the SQL |
| Who argues for it | Databricks calls the semantic layer “the linchpin” of agentic analytics; Thoughtworks recommends a governed semantic layer for agentic BI | SchemaChat (our approach): show the SQL, show a receipt, flag what wasn't covered |
Independent caution is worth reading. In its November 2025 Technology Radar, Thoughtworks places text-to-SQL at “Hold”, warning that language models frequently hallucinate when they have limited understanding of a schema or domain, that non-deterministic output makes errors hard to debug and audit, and that human review is required for all generated queries.
Our view: both approaches need the same safeguards — visible queries, human review where it matters, and honest reporting of what was not checked — and a team without a data department often cannot afford a semantic-layer project first.
How agentic analysis goes wrong, and what catches it
| Failure | Example | Check |
|---|---|---|
| Wrong join or double counting | A join repeats rows, so revenue doubles | Read the SQL; compare row counts in the receipt |
| Silent extra filter | The AI adds “IS NOT NULL”, which changes a count | Receipts list every filter applied; any you didn't ask for is flagged |
| Missing data treated as zero | Blank cells counted in an average | Ask for missing-value counts first |
| Small samples read as trends | A “winner” among groups of 5 rows | A caution when groups are small or the top two are within a few percent |
| Invented figures in the write-up | A percentage that appears in no query result | The write-up is rejected if its numbers aren't in the step data |
None of these is theoretical: they are the kinds of defects our own test suite has caught. See how we test an AI data analyst for the method and the failures.
When to use agentic analysis, and when a dashboard is better
- Use an agent for open questions, root-cause hunts and “what changed?” investigations.
- Use a dashboard for numbers you are accountable for every week and for audiences that need the same view every time.
- If you ask the same question three weeks running, turn it into a dashboard or an alert.
How to try it safely
- Connect with a read-only database user (a how-to is in the chat-with-your-database guide).
- Start with five questions whose answers you already know.
- Read the plan and the SQL before trusting the summary.
- Check the “Not covered” line.