Agentic analytics uses AI agents to interpret a business question, plan the analysis, run queries across data sources, and iterate on follow-ups. This post explains how it differs from a simple chatbot and from traditional business intelligence (BI). It also covers why agents sometimes give confidently wrong answers if not properly constructed and how a strong data foundation helps. You’ll learn how agentic analytics differs from agentic data and why missing context causes failures.
Key takeaways
- Agentic analytics is a form of analytics in which an agent plans, queries, iterates, and can act, while a human guides the work instead of writing every query.
- Missing business context is a common failure mode, so grounding agents in shared definitions is the core discipline.
- Agents should query data where it lives, under existing security.
- To start, pick one domain, publish certified data products, and put an agent on top.
What agentic analytics means
Agentic analytics shifts the human role from writing SQL queries to guiding autonomous AI systems. Given a business question, an agent builds an execution plan, queries the necessary sources, validates its intermediate results, and iterates until it finds an answer.
The key distinction between a simple chatbot and an analytical agent is autonomy in execution. A chatbot merely translates a prompt into a single SQL query. An agent actively plans, discovers relevant tables, cross-checks outputs across systems, and even triggers downstream workflows when authorized.
For example, answering “Was last week’s promotion on eggs profitable?” requires connecting three separate systems:
- Sales transactions
- Loyalty program redemptions
- Supplier wholesale costs.
An analytical agent locates these datasets, joins them across source boundaries, and verifies the final margin calculation before delivering the result.
Agentic analytics vs. agentic data
Agentic data and agentic analytics are different things. We use the term agentic data for the trace data agents emit, including tool calls, reasoning, memory, and cost. It is our term rather than an industry standard. It is emitted by every agent you run, in every system the agent touches. Agentic analytics is agents analyzing your business data, and it depends on your governed analytics foundation. If your definitions or access policies are incomplete, agents can return wrong or ungoverned answers.
How agentic analytics differs from traditional BI
Traditional business intelligence relies on human analysts as implicit context filters. In the classic workflow, a question goes to an analyst, who selects the table, applies the right date filter, and enforces the correct revenue definition before writing SQL. Removing the analyst removes that human safety net. Without an explicit context layer to supply the logic analysts know through institutional knowledge, agents will query raw schemas and guess at business definitions.
The second shift is from informing to acting. Traditional BI is purely read-only, but autonomous agents can update records, open tickets, or trigger downstream workflows. This changes your governance model. Read-only SQL controls enforce security on the data query path, while operational actions require a separate, write-path control plane governed on its own terms. Access control is no longer just a read-only concern.
What stays the same
The same data discipline applies. You still need governed data products, quality expectations, and clear ownership. The consumer is now an agent, but the standard for the data is the same.
Why agents give confident wrong answers
Agents without a context layer pick a source and reason at full speed, with no catalog to check and no colleague to ask. If that source is wrong, the answer is wrong, and it still reads as confident. Consider churn. Your team defines “churn” as a cancellation within 30 days. An agent that lacks that definition queries the support table instead of the canonical subscriptions table. The query runs and the numbers look plausible, but the answer is wrong. Definitions cause the same trouble elsewhere. Revenue can mean recognized revenue, booked annual recurring revenue (ARR), or gross billings. All three are correct, and they’re different. “Active customer” works the same way. A human analyst asks which one you mean, and an agent often guesses. Often the cause is missing grounding. The success criteria for production agents are accurate, consistent, and auditable. Better models don’t fix missing definitions. A semantic layer and embedded governance are the remedy.
The context layer and agent grounding
Agent grounding means delivering business meaning, ownership, and freshness before the agent reasons. A context layer holds those definitions in one place where agents can read them. In this post, the semantic layer is the part of the context layer that holds shared business definitions, while the context layer also carries ownership and freshness. Build the context for one domain first, and then let agents query that domain.
Data products as the unit agents consume
Agents have no escape hatch when a definition is ambiguous. Each data product ships with documentation, an owner, quality expectations, and an access policy. Agents read that package as the product they query.
What the data layer needs for agentic analytics
Start with the context layer described above. Four more capabilities are needed, specifically federated access, an open data lakehouse, governance in the access layer, and a control plane.
Let’s look at them one by one.
Federated access instead of centralizing first
AI agents can be configured to query data where it lives, governed by your existing security protocols. Maintaining a federated, governed access layer allows agents to reach live data in place. This approach acts as an alternative to copying every dataset into a central store before enabling analytics, improving the slow, expensive, and unscalable nature of data centralization.
An open data lakehouse foundation
While federation connects remote sources, an open data lakehouse provides the high-performance storage layer for analytical datasets landed locally. Using open table formats like Apache Iceberg gives platform teams warehouse-level table features over object storage, ensuring data remains fresh, queryable, and reproducible for AI workloads.
To keep this foundation performant, table maintenance must run automatically in the background. Operations like file compaction, orphan file cleanup, and snapshot expiration prevent query performance degradation over time. Because historical auditability depends directly on snapshot retention, platform engineers should align snapshot expiration windows with corporate compliance policies so that past agent queries can be accurately reconstructed.
Finally, an open format guarantees interoperability. Keeping analytical tables in Apache Iceberg prevents vendor lock-in, ensuring your underlying data remains accessible to diverse analytical engines, external data tools, and evolving AI model stacks.
Governance inside the access layer
Governance must live directly at the point where agents connect to data. This means enforcing read-only access on the query execution path, applying role-based (RBAC) and attribute-based (ABAC) policies, and logging audit trails from day one. Detailed audit logs allow platform teams to reconstruct the exact SQL queries and tool invocations generated behind an agent’s answer.
This server-side enforcement is critical when working with standards like Model Context Protocol (MCP). MCP provides an open interface between AI agents and data tools, but its protocol-level read-only signals are purely advisory. True security depends entirely on what the server enforces.
Rather than relying on client-side cooperation, Starburst enforces read-only access on the server side by parsing incoming requests directly through the Trino SQL parser, rejecting any write operations or unauthorized statement types before execution.
An agentic control plane
As analytics expands from delivering insights to taking action, the platform needs an operational boundary to coordinate agents, tools, and execution workflows. An agentic control plane provides this policy boundary, governing the write-path actions that read-only query engines intentionally block. While query-path security dictates what an agent is permitted to see, the control plane determines what an agent is authorized to do, enforcing strict compliance guardrails whenever an AI system updates a record, opens a ticket, or triggers an external workflow.
What AIDA shows about the pattern in practice
AIDA is Starburst’s agent for natural-language analytics on governed data. It became generally available earlier this year on both Starburst Galaxy and Starburst Enterprise, so hybrid and on-premises teams can use it too. It sits on a governed context layer over federated data. We also expose that same layer to external agents through an MCP endpoint. Treat it as one illustration of the pattern. You ask a question and the agent draws on the context layer, including its semantic layer, to understand your business, so its answers use your definitions. Governance limits the agent to data its user is allowed to see. It can reduce reliance on static dashboards by offering conversational, governed analysis.
How to evaluate your readiness
Use this checklist to test your current environment.
- Can an agent reach all relevant sources, by federation or by landing data in Iceberg where that fits better?
- Are business definitions encoded where an agent can read them?
- Is query access read-only and policy-enforced, with any write actions on a separate governed path?
- Can you reconstruct the queries and tools behind an answer?
- Are your Iceberg tables maintained automatically?
- Do you have a set of golden questions with known-correct answers that you re-run as definitions, data, and models change?
Each “no” is a problem to fix in the data foundation before you scale agents. Start small. Pick one domain, publish certified data products, and put an agent on top. Then try it on your own data, score it against your golden questions, and expand once the answers hold up.
Next steps
Build on an enterprise data foundation, try the pattern in a free trial, and use the other guides and workshops when you expand.
FAQs
What is agentic analytics?
Agentic analytics is analytics in which an agent interprets a question, plans the analysis, runs queries across sources, and iterates on follow-ups. Where you allow it, the agent can also take action. A human guides the work instead of writing every query.
How is agentic analytics different from traditional BI and dashboards?
The traditional path runs from question to analyst to SQL to dashboard. With agents, the analyst’s filter is gone, so the data layer must supply table choice, date ranges, and definitions. Analytics also moves from informing to acting, so governance has to cover actions as well as reads. AIDA, for example, can reduce reliance on static dashboards by offering conversational, governed analysis.
Why do AI agents sometimes give incorrect answers using enterprise data?
Agents pick a source and reason at full speed, without asking anyone what a term means. If “churn” or “active customer” has no shared definition they can read, they may query the wrong table and still sound confident. Often the cause is missing grounding. Agent grounding delivers meaning, ownership, and freshness before the agent reasons.
Do I need to centralize data before enabling agentic analytics?
Not before you start. Agents can query data where it lives, in place, under your existing security. Copying everything into one store first is slow, expensive, and hard to scale. Federated, governed data access lets one agent join sources without copying them. Federation adds source load and join cost, so land frequently queried or performance-critical datasets in Iceberg when those costs start to matter.
How does MCP fit in, and how do you keep agent access governed?
MCP is an open protocol between agents and data tools. It doesn’t guarantee governance on its own, because its read-only signal is advisory and enforcement depends on each implementation. Put read-only access on the query path, with RBAC, ABAC, row-level security, and audit trails in the access layer.
How do data products help agents?
A data product gives the agent one certified source, with documentation, an owner, quality expectations, and an access policy.
