The term “AI” is everywhere, but it has become a confusing umbrella for a wide range of capabilities. We have chatbots that answer simple questions, copilots that suggest code, and generative tools that write prose. While powerful, many enterprises are finding these tools hit a ceiling. They are helpful, but they don't fundamentally change the workflow. This is because most AI today is reactive. It waits for a command and executes a single task. This has led many organizations into "pilot purgatory," where impressive demos fail to translate into scalable, operational value. The reason is simple: real data work isn't about single questions; it's about multi-step investigation and reasoning.
This is where agentic analytics comes in. An agentic analytics platform, like Cube D3, operates on a different paradigm. It doesn’t just answer; it reasons, acts, and learns. It’s a digital teammate, not just a tool. The future of work is agentic. We are seeing it transform software engineering, and now it's coming for analytics.
Reasoning: The Power of Deep Context
A simple chatbot might translate "show me revenue" into a SQL query. But what does "revenue" mean? Is it gross or net? Does it include returns? Is it based on cash or accrual accounting? A generalist AI will guess, and its guess might be confidently wrong, leading to the inconsistent metrics that plague data teams.
D3’s agents reason because they are grounded in the deep context of your business, provided by Cube Cloud’s universal semantic layer. They don’t just see a database schema; they see a curated model of your business. They know precisely how "revenue" is calculated, what defines an "active user," and which filters are appropriate for a given business question because you have defined it once in a central, governed location. This moves the AI from syntactic understanding (matching keywords) to semantic understanding (grasping business meaning). This is the foundation of trust. An agent that can reason with your business logic is an agent that produces reliable, defensible outputs.
Acting: From Single Queries to Multi-Step Analysis
The most significant leap from a chatbot to an agent is the ability to act on a goal through a series of steps. As Artyom demonstrated in the webinar, this is an interactive and iterative process. The workflow looked like this:
- Initial Prompt: "Show me revenue numbers." The agent queries the governed revenue metric.
- Follow-up: "Add a monthly breakdown." The agent adds the time dimension.
- Complex Derivation: "Now, add prior month's revenue and month-over-month growth."
This final step is where the agentic capability shines. The "month-over-month growth" metric didn't exist in the data model. Instead of failing, the AI Data Analyst agent acted as a true analytical partner. It understood the user's intent, formulated a plan, and executed a series of calculations on top of the governed metrics to produce the answer. It performed a multi-step task, moving from simple data retrieval to on-the-fly analysis. This ability to plan and execute complex workflows is what allows agents to automate tasks that would otherwise require hours of manual work from a human analyst.
Learning: Building Institutional Knowledge
Finally, an agentic platform must learn. If every analysis starts from scratch, you are simply speeding up redundant work. D3 is designed to build institutional memory, ensuring the system becomes smarter and more reliable over time. This happens through several mechanisms:
- Memories: D3 agents remember the context of past conversations. When a user provides feedback (a thumbs up or down), it reinforces which analytical paths were successful. The agent learns what a "good" answer looks like for a specific team or use case. Great analysts remember what worked. So do great AI teammates.
- Certified Queries: When an analyst crafts a particularly insightful or complex query, it can be "certified" as a gold-standard report. This becomes a reusable, trusted pattern that other users and agents can leverage, codifying best practices directly into the system.
The future of data work isn't just about getting faster answers. It's about augmenting data teams with AI teammates that can handle complex, multi-step analytical workflows with a deep understanding of the business. That is the core of agentic analytics, and it’s a fundamental redefinition of the entire analytics experience.
Next up: In our next post, we’ll explore the engine that makes this all possible: the universal semantic layer.
Curious to explore D3 further? Sign up for the waitlist or access the on-demand webinar.