Agentic BI

Knowi's Agentic BI turns natural language into action. Instead of manually building queries, selecting chart types, and configuring dashboards, you describe what you want and the AI handles it ? creating dashboards, generating queries, transforming visualizations, exporting data, and managing reports through conversation.

Agentic BI is available in two ways:

  • In-Product Agents ? AI assistants built into Knowi's dashboard and widget interfaces. See AI Tools for in-product documentation.

  • MCP Server ? Connect external AI tools like Claude, GPT, or Copilot to Knowi via the Model Context Protocol. Your AI assistant operates Knowi programmatically.

What You Can Do

Ask Questions About Your Data

Ask natural language questions and get answers with visualizations. The AI finds the right dataset, writes the query, and returns results.

"What were our top 10 customers by revenue last quarter?"
"Show me the trend in support tickets over the past 6 months"
"Which product categories have declining sales?"

Create Dashboards

Describe the dashboard you want. The AI selects relevant datasets, generates queries, picks appropriate chart types based on the data, and builds a complete dashboard.

"Create a sales performance dashboard with revenue trends, top products, and regional breakdown"
"Build an executive dashboard with KPIs for this quarter"

Create and Transform Widgets

Create new visualizations or modify existing ones through conversation.

"Make a pie chart of revenue by region"
"Change this to a stacked bar chart"
"Add a date filter to this widget"

Get AI Recommendations

Ask for insights and the AI analyzes your data to surface trends, anomalies, and actionable recommendations.

"What trends do you see in this data?"
"What's driving the revenue drop this month?"
"What should I focus on to improve conversions?"

Search Across Assets

Find dashboards, widgets, datasets, reports, and documents using keyword or semantic search.

"Find dashboards related to customer retention"
"Which datasets contain revenue data?"

Manage Reports and Alerts

Create, schedule, pause, and manage reports and alerts through conversation.

"Email this dashboard to the sales team every Monday at 9 AM"
"Create an alert when revenue drops below $10,000"
"Pause all weekly reports"

Export and Share

Export data and generate shareable URLs without navigating menus.

"Export this widget to Excel"
"Create a shareable URL for this dashboard"
"Export the dashboard to PDF"

How It Works

The Orchestrator

When you submit a request, the AI orchestrator analyzes your intent and routes it to the appropriate agent. For complex requests, multiple agents are chained together automatically.

For example, "Create a sales dashboard" triggers:

  1. Search ? finds relevant datasets
  2. Create Dataset ? generates queries if needed
  3. Create Dashboard ? builds the dashboard with optimal chart types and layout

Clarification Sessions

When a request is ambiguous, the AI asks clarifying questions rather than guessing. The conversation maintains context across multiple turns.

You: "Show me the sales data"
AI: "I found 3 sales datasets: Product Sales, Regional Sales, and Online Sales. Which one would you like to explore?"
You: "Product Sales"
AI: [generates visualization from Product Sales dataset]

Available Agents

The system includes specialized agents for different tasks:

Agent What It Does
NLPConverts natural language to SQL queries and executes them
Data AnalystLong-form Q&A on datasets and documents
Widget Data AnalystAnalyzes and transforms widget data
Create DashboardGenerates complete dashboards from natural language
Create WidgetCreates individual widgets with optimal chart types
Update Widget SettingsModifies chart types, colors, labels, and formatting
Create DatasetBuilds datasets by searching datasources and generating queries
Search AssetsSearches across dashboards, widgets, datasets, reports, and documents
Dashboard FilterCreates and configures dashboard-level filters
Dashboard SettingsModifies dashboard properties (name, theme, layout)
Dashboard SummaryGenerates narrative summaries across all widgets
Add Widget to DashboardAdds an existing widget to a dashboard
Widget LayoutRepositions and resizes widgets on the dashboard grid
Report DeliveryDelivers dashboards via Email or Webhooks
Report ManagementCreates, lists, edits, pauses, and deletes scheduled reports
Alert ManagementCreates, lists, edits, pauses, and deletes data alerts
Share DashboardGenerates shareable URLs for dashboards
Data ExportExports widget or dashboard data to CSV or Excel
RecommendationGenerates AI-powered insights and recommendations

Accessing Agentic BI

In-Product: Dashboard Agent

Click the AI Assistant icon in the top-right corner of any dashboard. This opens the Dashboard Agent panel where you can interact with the entire dashboard using natural language.

For detailed instructions, see Using the Dashboard Agent.

In-Product: Widget Agent

Click the AI Assistant icon on any widget. This opens the Widget Agent panel where you can ask questions, transform data, change visualizations, and export results for that specific widget.

For detailed instructions, see Using the Widget Agent.

Via MCP Server

Connect external AI tools (Claude Code, Claude Desktop, or any MCP-compatible client) to Knowi's MCP server. This gives your AI assistant access to 31 Knowi tools.

For setup instructions, see MCP Server and Claude Code Setup.

Enabling Agentic BI

Account Activation

Contact your Knowi account manager or email support@knowi.com to enable Agentic AI for your account.

AI Settings

Once enabled, navigate to Settings > User Settings > AI Settings and toggle on AI Agents Access.

*Note: This controls AI Agent functionality for your entire account. Disabling it will disable agentic features for all users.

Role Permissions

Control which users can access Agentic BI through role permissions. Navigate to Settings > User Settings > Roles tab. Under the AI category, enable:

  • Use AI Agent Assistant in dashboards ? access to the Dashboard Agent
  • Use AI Agent Assistant in widgets ? access to the Widget Agent

For more on roles, see User Roles.

Security and Data Privacy

What Data Does the AI See?

When an agent executes, it sends data to the configured AI model to generate a response. The amount of data depends on the operation:

  • Dashboard and widget creation: The AI receives dataset schema (field names, types) and a small sample of rows to select appropriate chart types and generate queries.
  • Recommendations and insights: The AI receives actual widget data rows, truncated to fit the model's context window. Data is iteratively reduced (removing rows) until it fits.
  • NLP queries: The AI receives dataset schema and field metadata to generate queries. Query results are returned directly from the database ? not routed through the AI.
  • Deterministic tools (list, get data, export, delete): These do not involve the AI model at all. They execute directly against Knowi's services.

Data Residency and AI Providers

There are two separate data flows to understand:

1. Knowi's internal AI processing ? When an agent needs AI reasoning (e.g., choosing chart types, generating SQL, producing recommendations), Knowi sends data to its configured AI model. This is controlled by your AI provider setting:

ProviderWhere Knowi Sends Data for AI ProcessingConfiguration
Knowi (Internal)Knowi's own infrastructure. Data stays within Knowi.Default ? no configuration needed
OpenAIOpenAI's servers via API.Settings > AI Settings > AI Model Providers
Anthropic (Claude)Anthropic's servers via API.Settings > AI Settings > AI Model Providers

AI provider settings are configurable per feature ? you can use the internal model for recommendations (which include data rows) and an external model for dashboard creation (which only includes schema).

2. MCP client data flow ? When you access Knowi through an external AI tool (Claude Code, Claude Desktop, GPT, Copilot, etc.), tool results ? including query results, dashboard metadata, and data rows ? are returned to that client. The client's LLM then sees this data. This is true regardless of which AI provider Knowi uses internally. Even if Knowi is configured to use its internal AI model, the MCP tool responses (which may contain actual data) are sent back to the calling client's LLM.

In practice, when using Claude Code with Knowi's MCP server, data flows through both Knowi and Anthropic's servers ? Knowi processes the request, and the tool response (containing your data) is returned to Claude, which runs on Anthropic's infrastructure.

For maximum data isolation, use Knowi's in-product agents (Dashboard Agent, Widget Agent) with the internal AI provider. Data stays entirely within Knowi's infrastructure.

For on-premises deployments, the AI model runs within your infrastructure. MCP access from external clients would still send tool responses to the client ? evaluate whether this is acceptable for your data classification requirements.

Authorization

All agents enforce Knowi's existing authorization model:

  • Resource-level access: Before operating on any dashboard, widget, or dataset, the system verifies the user has permission using user.isAllowed(resourceId, assetType, write). A user can only interact with resources they are explicitly authorized to access.
  • Customer isolation: Datasource access is verified by matching the datasource's customer ID against the user's customer ID. A user from Organization A cannot access Organization B's datasources, datasets, or dashboards through an agent.
  • Role-based permissions: Agentic AI access is controlled through role permissions. Admins can enable or disable dashboard-level and widget-level agent access per role.

Row-Level Security

AI agents respect user content filters. If a user has row-level security applied (e.g., they can only see data for their region), the agent only sees and operates on the filtered data. This applies to all agent operations including recommendations, data analysis, and NLP queries.

Audit Trail

All agent executions are logged with:

  • The user's input prompt
  • Success or failure status
  • Response message
  • Execution time
  • Associated resource IDs (dashboard, widget, dataset)

Logs are accessible to account administrators.

MCP-Specific Security

When agents are accessed via the MCP Server, additional protections apply:

  • Prompt injection detection: Instructions are scanned for injection patterns and blocked if detected.
  • Destructive operation safeguards: Delete/remove/drop operations are blocked in natural language tools and must use the explicit knowi_delete tool with confirmation.
  • Input sanitization: Control characters are stripped from all inputs. Instruction length is capped at 2,000 characters.
  • Tool whitelisting: Only the 31 registered tools can be called. Arbitrary tool names are rejected.

See MCP Server Security for full details.