Agentic BI isn't a tool. The truth is, it's a suite of tools connected together: models with a harness, data pipelines (ETL), data analytics (BI), and data storage. It's not just chatting with an LLM, even though it might look that way. Behind the user interface, it must contain performance and token monitoring, prompt audits, and other boring things to control agents and their activity.
On September 2nd, we’re hosting a live event "The Future of Data" with the RVA Data Enthusiasts in Richmond, Virginia. Peter Bilzerian, the U.S Managing Director of MPP Insights, will be leading this event with his insights on the state of the industry and will help prepare you for what the future will look like in data.
If you are in Virginia, attend the event now, as the number of attendees is limited. If you are not in Virginia, then let's have a quick look on Agentic BI in this blog post.
What We Will Cover in the Event
- What even is Agentic BI? - what does it do today and where's it going tomorrow?
- If you're in BI - is your job safe? - what agents will change, and what they won't
- Why the foundation matters more than the AI - we'll share real case studies we did - including the United Nations, and governments to get their foundations right.
- Where to start - how to tell if your data is ready for agents, and what to fix first
Let's have a quick look into Agentic BI.
What is Agentic BI?
BI tools came to help with visualization, analytics and reporting to answer questions like, how much did we sell?
Agentic BI is the new generation of the same tool, but it's built with an environment where AI becomes part of how you manage your business.
In agentic BI there are several agents, each built to handle one specific job. Every agent follows a set of instructions made for that exact task, so it knows exactly what to look for.
MPP BI: Agentic BI in Action
We've spent years building MPP BI, our enterprise BI platform for analytics, reporting, and visualization. Our focus from day one was performance and flexibility. That means no dependence on one cloud provider or one ecosystem. MPP BI runs on-premise, in the cloud, or both, with no limits either way.
The new generation of AI is agentic, and this doesn't mean a chatbot bolted on top of the dashboards that sends your data to a third-party AI model just to answer one question.
Agentic MPP BI is a full environment where agents connect to your data, your systems, and your documents, and do real work inside your business.
- Works with any LLM, whether it runs on your own servers, in the cloud, or a mix of both
- Pulls from any data source, including your CRM, databases, file storage, email, and scanned documents
- Works with any data type: text, tables, images, audio, and video
How Agents Work Inside MPP BI
An agent is a digital assistant built to represent your interests inside a system. In BI, an agent helps you understand what's happening in your business. It investigates your data, handles tasks for you, and builds or updates dashboards. We give you a full set of tools so your agent can do this work well and help you get more done.
RAG: How Agents Get the Right Context
Most AI models answer questions using knowledge they picked up during training. That training makes them capable, but it doesn't include your specific business data. So agents need a way to look up information beyond what they already know, things like your job, your data, and your daily work.
This is where RAG comes in. RAG stands for Retrieval-Augmented Generation. The idea is simple: instead of answering from memory, the AI looks up your actual data first, then builds its answer based on what it finds. This keeps your answers grounded in real facts, not guesses.
MCP: The Translator That Connects Your Tools
To connect an agent to your systems, MPP BI uses MCP (Model Context Protocol). MCP is a standard way for an AI model to talk to outside tools and data. It lets an agent read your CRM or your spreadsheets, without a developer needing to build a custom connection every time.
Skills: instructions for specific jobs
A "skill" is a set of instructions an agent follows for one particular type of task. For example, when a document is uploaded, the agent can activate the skill built for reviewing that kind of document, and it knows exactly what to look for.
Two ways an agent can operate:
- Review mode: The agent finds problems, explains them, and suggests fixes. A person still makes the final decision.
- Autonomous mode: The agent fixes the problems itself, following a set process, with no human step in between.
A Safe, Sandboxed Space for Agents
Agents run inside a sandbox, a closed space that can't affect anything outside it. Each agent only has access to what you've approved for it to use, and every action it takes gets logged and tracked. This setup lets a company run many agents at once without losing control over what they're doing.
See You in Richmond on September 2nd
"The Future of Data" will be held on Wednesday, September 2nd, in Richmond. Join us here.
Peter is bringing a limited edition of co-branded jars of chile garlic crunch, made with MarMar, a Richmond local brand.
If you can't attend the event and want to learn more about the Agentic features of MPP BI, and how AI functionalities can help your business, we invite you to have a 30-minute call and discuss it with our experts.
