Agentic analytics

What are agentic analytics?

Agentic analytics use agentic AI, a type of system that receives an objective and carries it through multiple steps of analysis without being re-prompted at each one. Unlike copilots that answer one question at a time, an agentic analytics system pursues a goal end-to-end: querying data, interpreting results, and surfacing insights on its own.

In embedded analytics platforms, agent analytics amplify the power of AI analytics to bring speed, trust, and consistency together inside a single governed system, where each capability reinforces the others.

How agentic analytics work

Agentic analytics work through a continuous loop of reasoning and action. AI agents evaluate incoming data against a defined goal, interpret what’s happening, and decide what to do next, without waiting for human direction at each step. The result is an agent analytics system that goes beyond reporting; it thinks, adapts, and responds.

This loop follows a consistent pattern:

• Interpret the objective: The agent takes in the business goal and translates it into an analytical problem to solve—understanding what success looks like before it starts.
• Locate and prepare the relevant data: The agent identifies which data sources are needed, pulls the right information, and structures it for analysis.
• Build and execute an analytical plan: Rather than running a single query, the agent sequences multiple steps—choosing methods, running analyses, and working through the problem systematically.
• Evaluate intermediate results and self-correct: The agent reviews its own findings at each step, adjusting its approach if the results are incomplete, unexpected, or point toward a different line of investigation.
• Surface a recommendation or trigger an action: Once the agent reaches a conclusion, it delivers an insight, recommendation, or automated action, without waiting to be asked.

Agentic analytics vs. AI copilots vs. traditional BI

Agentic analytics differ from traditional BI in fundamental ways, and are distinct even from AI copilots and conversational assistants. 

• Traditional BI puts the analytical burden on the human: you build the query, read the dashboard, and decide what it means. 
• AI copilots move things forward by letting you ask questions in plain language, but they’re still turn-by-turn: one question gets one answer, and you drive every next step. 
• Agentic analytics change the dynamic entirely. You set the goal; the system figures out how to reach it on its own.

What agentic analytics require

Agentic analytics are only as reliable as the data layer beneath them. An agent that reasons over poorly defined, ungoverned, or untraced data will produce fast answers—but not trustworthy ones. Before deploying agentic analytics in production, the following foundations need to be in place.

Modeled and governed data

Agents can’t build coherent analysis on raw, unstructured tables. The data an agent reasons over needs to be analytics-ready: cleaned, modeled, and documented with clear metric definitions and tested transformations. Without a foundation of governed data, the agent is improvising on top of your infrastructure, not reasoning with your business data.

A semantic layer

Business logic shouldn’t live inside the agent. A semantic layer gives the agent a shared, authoritative set of definitions, like what “revenue” means, how “active user” is calculated, which version of “churn” the business uses. The semantic layer ensures the agent inherits that context rather than inventing its own. Without it, faster answers just mean faster inconsistency.

Data lineage

Every output an agentic analytics system produces should be traceable back to the data and logic that generated it. Lineage makes that possible: letting teams inspect how a result was produced, verify which fields and transformations contributed to a number, and understand the downstream impact before making changes upstream. This is what separates governed agentic analytics from a system that simply sounds confident.

Human-in-the-loop oversight

Not every action an agent can take should be fully automated. For high-impact decisions (irreversible actions, instances where an error has catastrophic consequences), human review is a necessary checkpoint. Agentic analytics work best when autonomy is calibrated to consequence: more freedom for low-stakes exploration, more oversight where the cost of a wrong call is high.

Agentic analytics use cases

Financial services and banking

Financial institutions deal with enormous volumes of transactions moving in real time. And the window to catch fraud, flag anomalies, or surface compliance risks is narrow. Traditional reporting catches problems after the fact, when losses have already occurred and response options are limited.

Agentic analytics continuously monitor transaction streams and behavioral patterns across multiple data sources, identifying anomalies and escalating suspicious activities the moment they surface. Instead of waiting for an analyst to run a report, the agentic system works in the background: detecting, reasoning, and routing issues to the right teams without delay.

Retail and e-commerce

Retailers operate across a complex web of channels, inventory systems, and customer touchpoints. When conversion rates drop or demand shifts unexpectedly, diagnosing the cause typically means pulling reports from multiple sources and manually connecting the dots—a process that takes time businesses can’t afford.

Agentic analytics monitor customer journeys, funnel performance, and behavioral signals continuously, identifying where and why performance is slipping before it compounds. Rather than surfacing a dashboard for someone to interpret, the system identifies the likely cause and recommends a targeted response, whether that’s a pricing adjustment, a promotional trigger, or a change to the customer experience.

Manufacturing and supply chain

Unplanned downtime is one of the most expensive problems in manufacturing. Equipment failures and supply chain disruptions don’t announce themselves in advance. By the time a problem shows up in a report, the problem is already underway.

Agentic analytics monitor sensor data, equipment performance, maintenance history, and supplier data in real time, identifying early warning signs that would be easy for a human analyst to miss across thousands of data points. When an anomaly appears, the system doesn’t wait for someone to notice. It flags the issue, identifies likely failure points, and recommends action before production is affected.

SaaS and technology

For software companies, analytics are increasingly a product decision. End-users expect the tools they rely on to surface relevant insights within the product itself, not push them to a separate dashboard or make them wait for a data team to respond. Meeting that expectation with traditional BI requires constant engineering effort. Even conversational AI analytics still put the burden of back-and-forth querying on the end-user.

Agentic analytics change the equation. Embedded directly into a SaaS product, agentic analytics experiences give end-users a way to set a goal and receive a governed, multi-step stream of ongoing insights. No re-prompting, leaving the platform, or taxing internal engineering or data teams. The agent works from the semantic layer and business logic already defined in the analytics platform, so every answer it surfaces reflects consistent, approved definitions and permissions. The result is a SaaS product that doesn’t just show data: it reasons over it, responds to it, and delivers value continuously.

For software creators, the advantages compound. An agentic analytics platform simplifies the process of building and embedding analytics in product: less effort to build and maintain every new analytics feature, faster shipping of intelligent product experiences. Software creators have a lower lift while building a meaningful point of differentiation in a market where end-users expect more from their software. 

Implement agentic analytics with Sisense

Sisense gives you the governed foundation that agentic analytics require, with trusted answers delivered to your end-users. The Sisense semantic layer defines what your data means, with row-level security enforced at the query layer, and agentic AI embedded directly inside the products your customers already use. 

That’s the difference between AI your users go to and AI that lives inside what you ship. Sisense has run customer-facing analytics in production for over 20 years, so when your agentic analytics answer a question under your name, they answer it right. 

Discover Sisense Intelligence.