For decades, modern analytics has been a rallying cry for data teams seeking to break free from legacy BI tools. This era brought powerful advances from cloud data warehouses and ELT workflows to self-service BI platforms and dashboards promising agility and democratization. But in 2025, everything has changed. The modern data stack, once the future, is now beginning to show its age.

The rise of generative and agentic AI is exposing the limitations of today’s modern analytics tools. Business users still wait in line for answers. Data teams still manage backlogs. Dashboards still tell you what happened, but rarely why and never what to do next. In short: the modern analytics stack optimized for speed, not autonomy. What’s next? It’s called agentic analytics.

From Self-Serve to Self-Acting

Self-service BI was supposed to empower the business. Tools like Looker, Tableau, and Power BI made it easier to explore data. But they assumed users knew what to look for, how to filter it, and which charts to build. These tools provided more control but not always more insight.

Even though analytics and BI tools have gotten easier to use, average adoption by enterprise employees is between 25-32%, since 2007 according to Gartner. Year-after-year this number has remained relatively unchanged, indicating that a greater opportunity to democratize data exists within every organization.

Agentic analytics flips the model. Instead of expecting humans to define every input and interaction, AI agents act as autonomous teammates. They generate queries, explore patterns, surface anomalies, and recommend actions, all grounded in context and driven by logic.

Why “Modern” Starts to Feel Legacy

The tools we’ve long labeled “modern” were revolutionary in a world of on-premises databases and Excel spreadsheets. But they weren’t designed for the AI era. Many are now retrofitting “AI Assistants” or “natural language query” buttons. And while useful, these bolt-on features often lack the depth, trust, and automation needed for enterprise-grade decision-making.

Here’s what today’s BI tools often don’t do well:

  • Maintain consistent metric definitions across tools and teams
  • Understand natural language in a way that supports follow-up questions
  • Offer full explainability of how results were generated
  • Integrate into workflows to trigger actions beyond charts
  • Collaborate with other agents or systems

And perhaps most importantly, they don’t act autonomously. They require humans to click, filter, and configure every insight. Modern analytics tools helped us analyze faster. Agentic analytics helps us act smarter.

What Makes Agentic Analytics Different

According to Gartner, agentic analytics represents the evolution of augmented analytics. It does this by applying AI agents, which are software powered by generative AI (GenAI), including Large Language Models (LLMs) and other AI techniques, for data analysis. These AI agents are designed to autonomously decide what to do and when to do it, using planning, short and long-term memory, tools, and learnings.

Cube D3’s agentic analytics bridges the gap between your data and decisions with AI data agents, matching human roles in the data and analytics lifecycle. It doesn’t rely on dashboards and drag-and-drop interfaces. It leverages Cube Cloud’s universal semantic layer to ensure data integrity, and AI agents to automate exploration, decision-making, and even follow-up tasks.

Let’s break down what this means in practice:

1. Semantic Intelligence at the Core

Every D3 agent is grounded on Cube Cloud’s universal semantic layer. That means when the AI Data Analyst answers a question like “What was our churn rate last quarter?”, it’s not guessing or improvising. It’s using pre-approved metric definitions, governed access controls, and consistent data logic.

Now, compare that to traditional NLQ add-ons in BI tools, which often misinterpret business terms or operate outside governance policies. In fact, these can be confidently and convincingly wrong.

2. Agents That Understand, Reason, and Explain

Agentic analytics is more than query generation. D3 agents can explore trends, suggest root causes, recommend next best actions, and most importantly, explain their logic and assumptions That last part is key. In an enterprise context, trust is everything. D3 doesn’t just show you an insight. It shows you how it got there with the semantic SQL query, metric definitions, and context—all fully explainable.

3. Built to Act, Not Just Analyze

The most popular BI tools available today were built to visualize. Agentic analytics platforms are built to integrate. With D3, agents don’t live in isolation. They can participate in your workflows, trigger actions in external platforms via MCP, and work alongside other agents via A2A. It’s not just about seeing the data. It’s about doing something with it in real time.

Agentic Analytics in Action

Cube D3 is not just faster; it’s transformational. Let’s say your VP of Sales asks, “Why did revenue from the West Coast spike in Q3?”

In a traditional BI tool, you would:

  1. Open a dashboard, but which one?
  2. Filter by region and time
  3. Hunt through dimensions and drilldowns
  4. Maybe identify a few contributing factors
  5. Possibly export the data and explain it in email or PowerPoint

With D3, a human simply asks the question into the Analytics Chat Interface. The AI Data Analyst:

  1. Interprets the question using business logic from the semantic layer
  2. Generates a governed semantic SQL query
  3. Returns a chart and explanation within seconds
  4. Offers follow-ups: “Would you like to see the top contributing SKUs?”
  5. Suggests next steps: “There was a price drop in July—should I compare impact across other regions?”

And the shift isn’t only for data analysts and business users. Now engineering workflows can be completed with fewer errors and faster delivery, providing more time for data engineers to focus on strategic work.

Traditional data engineering means:

  1. Writing SQL transformations by hand.
  2. Managing models across tools.
  3. Waiting for pull requests to deploy.
  4. Chasing down metric drift.

With D3, you describe what you want. The AI Data Engineer:

  1. Builds and validates the transformation logic.
  2. Updates the semantic model.
  3. Flags dependencies and impact.
  4. Documents everything—automatically.

The Bottom Line Modern analytics served us well, but the world has changed with the fast pace of AI innovations. What we need now is something smarter and more aligned with how people actually work.

Agentic analytics brings the next leap because it:

  • Understands natural language
  • Automates trusted insights
  • Enforces semantic governance
  • Integrates across tools and workflows
  • Explains its decisions and acts on them

That’s what agentic analytics delivers and why it’s the new definition of modern. With Cube D3, your AI teammates don’t just help you analyze. They help you act, explain, and scale AI adoption confidently. Be among the first to gain Cube D3 access by joining the waitlist and upgrading the data and analytics lifecycle for your data teams.