Why Sigma is winning the race for agentic analytics

Agentic analytics in Sigma is changing how you work with data.

Instead of waiting for dashboards or asking analysts for help, you can ask questions, explore data, and get answers in real time. AI agents help you think through the data, not just look at charts.

What is agentic analytics?

Agentic analytics is a new way of working with data where AI plays an active role in analysis instead of acting as a passive helper.

In traditional BI, you open a dashboard, look at pre-defined charts, and hope the answer you need is already there. If it is not, you ask a data team to build something new. That process is slow and breaks momentum.

Agentic analytics or agentic BI works differently.

You ask a question in natural language, just as you would ask a colleague. An AI agent interprets that question, queries the data, and returns an answer. More importantly, it understands context and can suggest follow-up questions or new angles to explore. This creates an ongoing conversation with your data.

Agentic BI shifts analytics from static reporting to active reasoning. The system helps you think through the data by exploring patterns, checking assumptions, and guiding you toward insights. You are no longer limited to what someone decided to build in advance.

This matters because business questions are rarely linear. You start with one question, learn something new, and immediately want to dig deeper. Traditional BI struggles with this. Agentic analytics is designed for it.

Sigma Computing aligns closely with this model. Rather than layering AI on top of old dashboards, Sigma enables agentic analytics as a core experience. The platform supports open-ended exploration on live data, which is essential for AI agents to reason effectively.

Why agentic BI needs a different analytics platform

Agentic BI cannot work well on top of legacy analytics tools.

Most traditional BI platforms were built for a different era. They assume that analysts define questions in advance, model the data, and publish dashboards for others to consume. This works for reporting, but it breaks down when AI agents need to explore data freely.

Agentic BI requires speed, flexibility, and trust.

Many legacy tools rely on data extracts. This means data is copied, cached, and refreshed on a schedule. For AI-driven analysis, this is a problem. Agents need access to fresh data to reason correctly. Stale data leads to wrong conclusions.

Traditional BI tools also depend on rigid semantic layers. These fixed definitions limit how questions can be asked. AI agents struggle in these environments because they cannot easily adapt to new or unexpected questions.

This is where Sigma Computing is different.

Sigma is built directly on cloud data warehouses. Queries run live on platforms like Snowflake, BigQuery, and Databricks. There are no extracts and no waiting for refreshes. This live-query model is essential for agentic BI.

Sigma also prioritises transparency. Calculations look like spreadsheets, not black-box code. This matters because agentic analytics must be explainable. You need to see how results are produced so you can trust both the data and the AI.

Because of this architecture, Sigma provides the foundation that agentic analytics needs:

• Real-time access to trusted data.

• Flexible exploration without rigid constraints.

• Clear logic that both humans and AI agents can understand.

This is why Sigma is well suited for agentic analytics, while many older BI tools struggle to adapt.

Agentic analytics in Sigma with Ask Sigma AI

Ask Sigma AI is where agentic analytics becomes real for everyday users.

Ask Sigma AI is Sigma’s natural language interface. It lets you ask questions about your data in plain language and get answers directly from live warehouse data. You do not need SQL, dashboards, or pre-built reports to start exploring.

This matters for agentic analytics because agents need a way to understand intent.

When you ask a question, Ask Sigma AI:

• Interprets the meaning of your request.

• Translates it into queries on live data.

• Returns results you can inspect and validate.

What makes Ask Sigma AI agentic is that it supports conversation, not just one-off answers. After showing results, it understands context and allows follow-up questions that build on previous answers. This creates an analysis flow that feels collaborative rather than transactional.

Ask Sigma AI also prioritises trust and explainability. You can see the underlying logic and calculations behind the answers. This is critical for agentic BI, where AI must support decisions without becoming a black box.

Because Ask Sigma AI operates on Sigma’s live-query architecture, the insights it produces are always based on current data. This allows AI agents to reason accurately and react quickly as data changes.

Ask Sigma AI does not replace human judgment. Instead, it acts as an assistant that accelerates exploration, highlights patterns, and helps you decide what to look at next. This is the core promise of agentic analytics in Sigma.

Conclusion: the future of agentic analytics in Sigma

Agentic analytics in Sigma is not a concept or a future roadmap. It is already happening.

Sigma Computing brings together live data access, a transparent spreadsheet interface, and AI capabilities that support real reasoning. This combination is what makes agentic analytics practical, not theoretical.

With Ask Sigma AI, Sigma moves beyond traditional self-service BI. You can ask questions in natural language, explore data iteratively, and understand how results are produced. The AI works with you, not instead of you.

Many BI tools talk about AI. Sigma operationalises it.

Because Sigma runs directly on cloud data warehouses, AI agents always work with current data. Because calculations are visible, you can trust and validate insights. Because the interface is familiar, business users can adopt agentic BI without heavy training.

This is why Sigma is winning the race.

Agentic analytics requires a platform built for exploration, speed, and trust. Sigma Computing delivers all three. As AI agents become a standard part of analytics workflows, agentic analytics in Sigma sets the benchmark for how humans and AI should work together on data.