Agentic BI is a new category of business intelligence where AI agents handle the analytics workflow autonomously. Instead of users navigating dashboards, writing queries, and exporting reports, they describe what they need in plain English. AI agents identify the right data source, build the query, create the visualization, and deliver the answer. Knowi is one of the first platforms built around this model, with currently 15 specialized AI agents that operate across SQL, NoSQL, and API data sources without requiring a warehouse and new agents shipping regularly as the platform expands.
Quick Summary (TL;DR)
- Agentic BI uses AI agents to handle data querying, visualization, and insight generation autonomously, replacing manual dashboard workflows.
- Traditional BI requires months of data preparation (warehouses, ETL pipelines, transformations) before users can ask their first question.
- Knowi’s agentic BI platform currently has 15 AI agents including Query, Dashboard, Widget, Document AI, NLP Search, Recommendations, and AI Dashboard Generation.
- All AI processing runs on Private AI within your environment, so no business data is sent to public LLMs.
- Agentic BI connects directly to data sources (Elasticsearch, MongoDB, REST APIs, SQL databases) without requiring data movement.
- Users interact through natural language: ask a question, get an answer, request a formatted report, all in one conversation.
- Knowi’s agents can be embedded into SaaS products via API, so customers interact with analytics inside your application without a separate BI login.
Table of Contents
Why Traditional BI Forces Users to Adapt to the Tool
Traditional BI tools require all data to be consolidated into a single repository before analysis begins. If you have semi-structured data in Elasticsearch, structured data in a SQL database, and data flowing through REST APIs, the tool will not work until everything is in one place.
Getting there means building data warehouses, setting up ETL pipelines, and transforming data after it lands. That is a months-long infrastructure project before you see a single insight.
And after all that work, users still export dashboards to Excel. According to Gartner, fewer than 30% of BI deployments achieve widespread user adoption. The tool demands too much adaptation from the people who need the answers.
Static Dashboards Cannot Handle Real Workflows
Dashboards show data. They do not answer follow-up questions. They do not explain why a metric dropped. They do not format a snippet for a board deck.
Users need multi-step workflows: ask a question, dig into the cause, get a recommendation, and export a formatted report. Traditional BI handles the first step and makes users figure out the rest manually.
Users Need Answers, Not Interfaces
A VP of Product who wants to know why monthly active users dropped last week should not need to know which dashboard contains that data, how to set the right filters, or how to interpret nested visualizations. That is not analytics. That is training overhead.
What Is Agentic BI and How Does It Work?
Agentic BI replaces the manual steps in traditional analytics with autonomous AI agents. Each agent handles a specific part of the workflow: finding data, building queries, creating visualizations, surfacing anomalies, and generating reports.
The user’s role shifts from operating a tool to directing agents through natural language. The system adapts to the user instead of the other way around.
How Knowi’s production AI Agents Work Together
Knowi’s agentic BI platform currently includes 15 specialized agents that cover the analytics lifecycle including the below agents:
- Query Agent: Identifies the right data source and writes the query. Users describe what they need in plain English. The agent figures out which database holds the data and retrieves it.
- Dashboard Agent: Creates, modifies, and manages dashboards through natural language. Add filters, change settings, generate share URLs, export to PDF, PPT, or Excel.
- Widget Agent: Builds and configures individual visualizations. Adjusts chart types, formatting, and data mappings based on conversational requests.
- Document AI: Reads PDFs, Word docs, and images. Extracts structured data and makes it queryable alongside your databases. Supports cross-document search.
- NLP Search Agent: Enables natural language queries across all connected data sources without requiring a semantic model or predefined schema.
- Recommendations Agent: Analyzes data patterns, identifies anomalies, and surfaces insights users might miss by staring at charts.
- AI Dashboard Generation: Automatically generates complete dashboards from a dataset, selecting appropriate visualizations and layouts based on the data structure.
These agents work in sequence or in parallel depending on the request. A single question like “What tickets are coming up most often in our contact center, and how do we reduce high-priority ones?” triggers the Query Agent to find the data, the Recommendations Agent to analyze patterns, and the Dashboard Agent to format the output.
This is where Knowi is today. The agent library is actively expanding, with new capabilities shipping every few weeks as more of the analytics workflow becomes autonomous.
How Is Agentic BI Different from Adding AI to a Dashboard?
Most BI vendors now offer an “AI assistant” bolted onto their existing dashboards. That is not agentic BI. The distinction matters because it determines what users can actually do.
A chatbot layered on top of a dashboard can answer questions about what is already on screen. An agentic system can find data across sources, build new queries, create new visualizations, and execute multi-step workflows without the user touching the interface.
| Capability | Traditional BI | BI with AI Chatbot | Knowi Agentic BI |
|---|---|---|---|
| Data preparation | Requires warehouse and ETL | Requires warehouse and ETL | Connects directly to sources, no data movement |
| Query creation | User writes SQL or uses drag-and-drop | AI suggests queries on current dashboard data | Query Agent writes queries across any connected source |
| Data source scope | Modeled data in warehouse only | Current dashboard scope only | All connected sources: SQL, NoSQL, APIs, documents |
| Visualization creation | Manual chart building | Limited chart suggestions | Dashboard and Widget Agents build and modify visualizations |
| Multi-step workflows | Not supported, users export to Excel | Single question-answer only | Conversational: question, follow-up, recommendation, export |
| Unstructured data | Not supported | Not supported | Document AI reads PDFs, images, and files alongside databases |
| AI privacy | No AI | Data sent to third-party LLM | Private AI processes data within your environment |
| Embeddable | iframes with limited multi-tenancy | Limited or not available | Full API: embed agents into your product with row-level security |
Why Private AI Matters for Agentic BI
Agentic BI only works if the AI can access your actual business data. Public LLMs cannot do this. They do not have access to your databases, and sending proprietary data to external models creates compliance and security risks.
Knowi’s agents run on Private AI within your environment. No data leaves your infrastructure. For teams in healthcare, finance, or government, this is not optional. It is a prerequisite.
Knowi also supports multi-model AI: teams can choose between Claude, OpenAI, or Knowi’s in-house AI for each feature. Healthcare teams that need data containment use Knowi AI. Product teams that want frontier model capabilities use Claude. The choice is per-feature, not platform-wide.
Agentic BI as an Embedded Service Layer
The vision for agentic BI extends beyond Knowi’s own interface. SaaS companies can embed Knowi’s agents into their own products via API.
A customer’s end user interacts with a chat interface inside your application. Knowi processes the request behind the scenes and returns the answer. No separate login. No context switching. No BI tool to learn.
This turns analytics from a destination (a dashboard portal users visit) into a service layer (intelligence embedded wherever users work). According to G2’s Embedded Analytics category, demand for analytics embedded directly into SaaS products grew significantly in 2025 as companies shifted away from standalone BI tools.
This is where Knowi is today. The agent library is actively expanding, with new capabilities shipping every few weeks as more of the analytics workflow becomes autonomous.
Ready to see agentic BI in action? Schedule a demo and see how Knowi’s AI agents work with your data.
Frequently Asked Questions
What is agentic BI?
Agentic BI is a category of business intelligence where AI agents autonomously handle data querying, visualization, insight generation, and report delivery. Users interact through natural language instead of navigating dashboards or writing queries.
How is agentic BI different from a BI tool with an AI chatbot?
A BI chatbot answers questions about data already displayed on a dashboard. Agentic BI agents find data across sources, build new queries, create visualizations, and execute multi-step workflows without requiring the user to touch the interface.
Does agentic BI require a data warehouse?
No. Knowi’s agentic BI connects directly to SQL databases, NoSQL systems like MongoDB and Elasticsearch, REST APIs, and unstructured documents. There is no requirement to move data into a centralized warehouse before analysis.
Is agentic BI secure for sensitive data?
When built with Private AI that runs within your environment, agentic BI maintains full security and governance. Knowi’s agents process data locally. No business data is sent to external LLMs. This is critical for HIPAA, SOC 2, and other compliance frameworks.
Can I embed agentic BI into my own SaaS product?
Yes. Knowi’s agents are accessible via API, allowing SaaS companies to embed conversational analytics directly into their products. End users interact with AI agents inside your application with full multi-tenant isolation and row-level security.
What AI models does Knowi’s agentic BI support?
Knowi supports multi-model AI. Teams can choose between Claude (Anthropic), OpenAI, or Knowi’s in-house AI on a per-feature basis. This avoids vendor lock-in and lets teams match the model to their compliance requirements.
How many AI agents does Knowi have?
Knowi has currently has 15 AI agents including Query Agent, Dashboard Agent, Widget Agent, Document AI, NLP Search Agent, Recommendations Agent, and AI Dashboard Generation. Each handles a specific part of the analytics workflow and they work together on complex requests.