Introduction to Agentic Analytics

Modern businesses generate more data than ever. They gather information from websites, financial systems, CRMs, supply chains, marketing platforms and more. To make sense of it all they hire specialists, tasked with turning their data into insights. But, traditional analytics is pull-based: you must know what to look for, when to look, and how to interpret it. Even with modern tools, people remain the bottleneck. The explosion of metrics, dimensions, and data sources makes it nearly impossible to monitor everything. As a result, issues go unnoticed, opportunities are missed, and critical trends surface only in hindsight.

What if analytics worked the other way around? What if your data was monitored continuously, relevant analysis ran automatically, results interpreted instantly and you were notified only when something truly mattered? This is the promise of Agentic Analytics: a new approach that uses AI agents to perform real-time analysis, interpret results and proactively deliver only the insights that matter. No more dashboard diving. No more noise. Just intelligence that comes to you.

A Brief History of Agentic AI

We didn't arrive at "agentic AI" overnight, it's the result of several big shifts in how we build and use intelligent computer systems. In the early days of computing, from the 1950s through the 1990s, computers were set up to follow fixed rules. You told them exactly what to do, and they did it. These systems couldn't adapt, learn or pick up new tasks on their own.

Over time we developed smarter systems that could detect and learn from patterns by giving them lots of examples. This opened the door for more flexible artificial intelligence, like recognising speech, knowing what is being shown on an image, or what to recommend to potential buyers online.

Then, around the 2020s, we arrived at another major leap: large language models (LLMs). LLMs are AI systems trained on huge amounts of text so they can understand and generate natural language, like English and Dutch. They not only answer questions, but can summarise, translate, draft content and respond in a conversational way. But, LLMs still wait for your instructions and follow them as best they can. They do not plan, act, or connect to the outside world.

That's where agents come in, a recent breakthrough in the evolution of AI. An AI agent extends the capabilities of an LLM with memory, planning logic, and tools. These features add awareness of context, the ability to decide what to do next and the means to act appropriately. This new generation of AI can trigger workflows, gather information, interpret results, and make decisions with minimal human input. It's a shift from systems that simply respond to us, to ones that actively work alongside us. That's what we mean by agentic AI.

Why This Matters for Analytics

Traditional analytics puts the burden on people to find insights. You need to know what to look for, where to find it, and how to interpret it. This ongoing process is repetitive, reactive, and can be slow. By the time an issue is spotted, the opportunity to act may have already passed, or have a diminished impact.

Agentic analytics turns that around. Instead of you chasing the data, the data comes to you. An agent can continuously monitor large volumes of data and decide on the right type of analysis for each situation, whether that means spotting unusual patterns, comparing groups, or identifying possible causes. It runs these analyses, explains the results in clear and simple language, and summarises why something changed and how important it is. Most importantly, it notifies you only when the insight truly matters.

Instead of waiting for people to ask the right question, with agentic AI we now have the ability to ask and answers the questions automatically. It's the next step in analytics evolution: from manual reporting, to real-time dashboards, to real-time understanding.

Here's what that means in practice:

• Proactive Insights: Agents monitor data continuously and intelligently alert you only when something meaningful happens, so you don't have to check dashboards just to stay informed.
• Noise Reduction: Most alert systems simply point out a change, like "sales dropped 5%." Agentic analytics goes further by explaining why it happened, how severe it is, what it affects, and what you can do next. The result is less noise and more clarity.
• Consistent, Reliable Interpretation: Every analyst explains data differently. Agents can apply the same reasoning every time, providing consistent trend analysis, explanations, and confidence levels you can trust.
• Scalability: A person can track a handful of metrics. An agent can track thousands, continuously and accurately, without losing focus or energy.
• Get Insights Faster: When something important happens, you receive a short summary, an interpretation with confidence level, and a link to the full analysis. That means the time between discovery and decision is dramatically reduced.
• Better Use of Analysts' Time: When analysts no longer spend hours chasing anomalies, they can focus on the work that matters, guiding strategy, supporting decisions, and improving how analytics delivers value. Agentic analytics doesn't replace the analyst; it makes them more effective.

Agentic AI Comes With Complexity

Agentic AI opens the door to a new kind of intelligence, but with that power comes a new layer of complexity. Building systems that can plan, decide, and act on their own is no small task. For agentic analytics to be work, it must balance autonomy with accuracy, and speed with trust.

One of the biggest challenges is accuracy. Large Language Models can sometimes make confident but incorrect assumptions or fill in missing details that are not supported by data. This is called hallucinating. Without a strong foundation of verified information, even advanced models can produce results that sound right but are wrong in subtle ways.

Another challenge is trust. When AI begins acting like an analyst, its work must be held to the same standards as one. Every conclusion should be transparent, every reasoning step explainable, and every action traceable. People need to understand and be able to verify how an agent arrived at a result before they can confidently rely on it.

Finally, everything depends on data quality. An agent is only as good as the data it has access to. If that data is incomplete, inconsistent, or poorly modeled, the insights it produces will reflect those flaws. Clean, structured data is the foundation that turns autonomy into reliability.

Recognizing and addressing these challenges is what separates experimental systems from dependable ones. Building guardrails, validating every step, and maintaining human oversight ensures that agentic AI enhances decision-making instead of undermining it.

What We're Building

Agentic analytics has enormous potential, but it only works when built on strong foundations. We've been working with AI on a technical level since 2019, and our approach is designed to make agentic analytics both powerful and trustworthy. We're on a mission to deliver meaningful autonomous insights, while staying grounded in data quality, transparency, and human oversight.

Strict Data Grounding
Every agent operates on top of the clearly defined data models, metrics, business rules, and definitions of your source systems. We make sure that you are able to trace back all insights to verified data, ensuring that what the AI produces is as accurate as the underlying source.

Structured Analytical Workflows
Agents don't improvise their approach. Analyses follow well-defined, known sequences. We leverage structure to guarantee consistency, auditability, and reliability across our results.

Relevance Scoring
Not every change deserves attention. Each potential insight is scored based on its importance, impact, confidence, and novelty. Only those that pass our threshold are shown to the user; everything else remains silent. The outcome is focus without noise.

Layered Interpretation
Each surfaced insight is designed to be instantly useful, and fully explainable. Users receive a short, clear summary of what happened, the reasoning behind it, and a link to the complete analysis for deeper exploration. The system delivers both immediacy and depth, depending on what the moment requires.

Human-in-the-Loop Learning
Your feedback (whether they mark an insight as "useful" or "not relevant") feeds back into the system. This helps us to refine our ranking and interpretation over time, ensuring that every cycle makes them smarter and more aligned with real business priorities.

By combining these principles, we create agentic analytics that are not just autonomous but also accountable. A system that acts intelligently, explains itself clearly, and earns trust through consistency and transparency.

Frequently Asked Questions About Agentic Analytics

What is Agentic Analytics?

Agentic Analytics is a new approach to business intelligence that uses AI agents to continuously monitor your data, automatically perform analyses, and proactively deliver only the insights that matter. Instead of requiring you to check dashboards or run reports, Agentic Analytics brings intelligence directly to you—typically via email or notifications.

How does Agentic Analytics differ from traditional analytics?

Traditional analytics is pull-based: you need to know what to look for, when to look, and how to interpret the results. Agentic Analytics is push-based: AI agents work continuously in the background, identify what's important, and notify you only when something requires your attention. This eliminates dashboard fatigue and ensures you never miss critical insights.

Is Agentic Analytics suitable for business leaders and executives?

Yes, Agentic Analytics is specifically designed for business leaders who need actionable intelligence without the complexity of traditional analytics tools. It delivers clear, concise summaries with explanations of why something matters and what actions you can take—perfect for executives who need insights, not data dumps.

What types of insights does Agentic Analytics provide?

Agentic Analytics can identify trends, detect anomalies, surface risks, highlight opportunities, and explain the business impact of changes in your metrics. Each insight includes context about why it matters, how severe it is, and recommended actions you can take.

How accurate is Agentic Analytics?

Agentic Analytics is built on strict data grounding principles, ensuring all insights are traceable to verified source data. The system uses structured analytical workflows and relevance scoring to ensure only high-confidence, meaningful insights are delivered. Human feedback continuously improves the system's accuracy and relevance.

Do I need technical expertise to use Agentic Analytics?

No. Agentic Analytics is designed for business leaders, not data scientists. Insights are delivered in plain language with clear explanations and actionable recommendations. You don't need to understand SQL, data modeling, or statistical analysis.

Conclusion

Agentic Analytics represents a major leap forward: AI that performs autonomous analyses, interprets results, and notifies you only when it matters.

It turns analytics from a reactive, human-driven process into a proactive, AI-driven system that works in the background — continuously, intelligently, and reliably.

We're working hard building this future. And if you want analytics that finally works for you instead of the other way around, agentic AI is the path forward.