Modern BI changed the way we think about data access, but even the most advanced dashboards and self-serve tools are starting to show their limits. As AI reshapes the enterprise software landscape, static dashboards and spreadsheet workflows are no longer enough.

What organizations need next is agentic analytics, an entirely new paradigm where AI-powered teammates work alongside humans to explore data, build models, generate insights, and explain their decisions.

This is exactly what Cube D3, the agentic analytics platform from Cube, is designed to deliver. Built on Cube Cloud’s universal semantic layer, D3 empowers organizations to move from a dashboard-driven culture to an agent-driven one—without compromising governance, consistency, or control.

So how do you transition? Whether you’re relying on a mix of traditional BI tools and spreadsheets or struggling with backlog and bottlenecks, this guide provides a step-by-step framework for adopting agentic analytics in a repeatable, scalable way.

Step 1: Identify High-Impact Use Cases

Start where repetitive questions slow down productivity

Every organization has recurring analytics tasks that eat up time: marketing campaign summaries, weekly sales breakdowns, inventory audits, or customer behavior trends. These are exactly the types of use cases that Cube D3’s AI Data Analyst can tackle from day one.

Begin your journey to agentic analytics by identifying:

  • Common ad-hoc requests from business stakeholders
  • Reports that require multiple SQL queries or iterations to build
  • Metrics that are consistently re-analyzed in different contexts

With Cube D3, business users can type natural language questions—like “Which regions had the highest margin last quarter?”—and get visual, governed results instantly. This alone can cut reporting cycles from days to minutes.

📌 Tip: Prioritize use cases where automation reduces the load on your data team and improves time-to-insight for business users.

Step 2: Build the Semantic Foundation

Agentic analytics requires a semantic layer—and Cube Cloud provides it

AI agents are only as effective as the context they’re grounded in. If your AI is pulling from raw tables or inconsistent logic, you’ll get fast answers—but they won’t be trustworthy or consistent.

That’s why Cube Cloud’s universal semantic layer is so critical to D3’s architecture. It acts as the business logic brain for all AI-generated outputs—ensuring that every chart, metric, and recommendation comes from a governed, centralized source of truth.

Steps to get started with Cube Cloud:

  • Define critical business metrics, dimensions, and data relationships
  • Model them in Cube Cloud using YAML, JSON, or code-based workflows
  • Apply role-based access controls and lineage to enforce governance

When D3’s agents query data, they don’t guess—they generate semantic SQL grounded in these models, ensuring accuracy, explainability, and auditability every time.

📌 Tip: Start by aligning with business stakeholders on critical KPIs and definitions. Then translate these into semantic models in Cube Cloud. Treat the semantic layer as living documentation that agents and humans alike can depend on.

Step 3: Onboard Your First AI Teammate

Activate Cube D3’s analytics agents and start exploring

With semantic models in place via Cube Cloud, you can now deploy your first agents in Cube D3. These agents are more than chatbots—they’re intelligent AI teammates that assist with analysis, model maintenance, and workflow execution.

Start with:

  • AI Data Analyst: For business users and analysts to query data using natural language, generate visualizations, and save reusable reports.
  • AI Data Engineer: For data stewards to automate semantic model creation, propose new metrics, and reduce backlogs.

Cube D3 provides a conversational interface where users can ask questions, inspect SQL, drill down into data, and iterate—all without waiting on a ticket or change request.

📌 Tip: Choose a business team (e.g., Sales Ops or Marketing Analytics) and guide them through a defined use case. Track adoption and satisfaction as part of your success criteria. Document data points like time to complete manually versus with agents to build your business case.

Step 4: Monitor Usage and Improve Agents

Refine the experience using transparency and governance features

Adopting agentic analytics isn’t a “set it and forget it” initiative. Success comes from governed iteration—observing how users interact with Cube D3 and improving both the agents and underlying models over time.

With Cube D3:

  • Every query is fully traceable and can be audited
  • Reports can be verified by admins to ensure reuse across teams
  • Query logic, filters, joins, and sources are always visible and inspectable
  • You can drill down into agent-generated results or override logic when needed

By using Cube Cloud’s semantic layer as a governance backbone, you can improve trust in AI outputs while still delivering speed and flexibility.

📌 Tip: Interview users about where they still encounter friction. Are definitions unclear? Are agents over- or under-confident? Use this data to tune prompts and improve semantic coverage.

Step 5: Scale Across Tools, Teams, and Agents

Extend agentic analytics into your existing workflows

Once you’ve validated the value of Cube D3 within a small team or use case, it’s time to embed agentic workflows across the enterprise. This means putting D3’s agents in the tools your teams already use—without requiring a context switch.

Examples:

  • Embed the Analytics Chat Interface in your intranet, apps, and tools
  • Use Model Context Protocol (MCP) or iframe embedding to integrate with external tools
  • Allow D3 agents to participate in multi-agent workflows, driving outcomes with agents from other domains via A2A (agent-to-agent)

This is where the full power of agentic analytics shows up: not just answering questions, but accelerating action, collaboration, and impact.

📌 Tip: Identify the workflows and daily tools your business teams already rely on. Add Cube D3 agents directly into them to minimize change management and maximize adoption of agentic workflows.

Begin a New Chapter in Analytics Powered by Cube

Traditional BI got us dashboards. “Modern BI” added interactivity. But now, Cube D3 with Cube Cloud is ushering in a new era: agentic analytics, where AI coworkers automate routine tasks, help data teams scale, and enable business stakeholders to get answers faster.

To recap the journey:

  • Identify high-friction workflows where AI can deliver value quickly
  • Model your business logic in Cube Cloud’s universal semantic layer
  • Deploy your first AI coworkers using Cube D3’s analyst and engineering agents
  • Govern, monitor, and explain every result to build trust
  • Scale across your org by embedding D3 into the tools people use every day

By transitioning to agentic analytics, you don’t just get faster reports. Now you can enable every team to act on data with confidence, speed, and autonomy.

Ready to take the next step? Explore how Cube D3 and Cube Cloud can help your organization embrace agentic analytics at cube.dev/product/cube-d3.