Agentic BI tools are analytics platforms where AI agents query data, build dashboards, and surface insights without waiting for a user to ask. The leading platforms in 2026 are Knowi, ThoughtSpot, Microsoft Power BI with Copilot, Tableau Pulse, Sigma Computing, Domo AI, and Tellius, each approaching “agentic” differently in terms of architecture, data source support, and how much the AI actually does on its own.
Quick Summary (TL;DR)
- Most BI platforms today offer AI copilots or assistants rather than fully autonomous agentic workflows. Truly agentic tools execute multi-step workflows, surface insights proactively, and retain context across queries.
- The biggest technical split is AI architecture: most BI tools layer AI on top of dashboards and semantic models, while a smaller group integrates AI directly into the query and data layer.
- Most BI platforms require data to be moved into SQL-based warehouses before analysis, with limited native support for NoSQL and APIs. Teams on non-SQL stacks should verify this before evaluating.
- Private AI (no data sent to external LLMs) is a hard requirement for healthcare, finance, and companies with data residency rules. Only a small subset of BI platforms support fully private, on-prem AI deployments.
- Gartner projects that 40% of enterprise applications will integrate task-specific AI agents by end of 2026, up from under 5% in 2025. Choosing a platform with native agent architecture now avoids a migration later.
Table of Contents
What Makes a BI Tool Genuinely Agentic?
The word “agentic” is applied to everything from a Copilot button in a dashboard to fully autonomous multi-step analysis. For a comparison to be useful, the criteria need to be specific. Three capabilities separate agentic BI from AI-assisted BI.
Proactive delivery: The system surfaces findings without being prompted. A tool that only answers questions on demand is AI-assisted, not agentic. Multi-step reasoning: The AI executes a sequence of actions: querying a source, detecting an anomaly, cross-referencing a second source, and formatting the result, without returning a single-step answer. Persistent context: The agent retains context across interactions, including the data model and user preferences, so each response builds on the last.
Most platforms in 2026 meet one or two of these criteria. Understanding how deeply a tool meets all three is the most useful frame for evaluation. To understand how agentic BI differs from traditional BI at the architecture level, that distinction goes deeper than feature lists.
The 7 Best Agentic BI Tools in 2026
Knowi
Knowi is built around 20+ dedicated AI agents that sit inside the data layer rather than layering AI on top of existing dashboards. The agents have direct access to schemas, field types, and the query engine, which means they execute against the actual data structure rather than guessing at it. This is the core architectural difference from tools that add AI as a post-query interpretation layer.
The platform supports deployment in controlled environments where data does not leave the organization’s infrastructure. Native connectivity covers MongoDB, Elasticsearch, Cassandra, InfluxDB, DynamoDB, and 55+ SQL and API sources, with cross-source joining available without ETL or a staging warehouse.
Specific agents include a dashboard agent (builds and modifies dashboards from natural language), a widget agent (generates individual chart components), a Query Agent (writes and executes queries), and a Document AI agent (queries PDFs and files alongside database data). Agents can be run through Slack via a /knowi command or embedded into third-party SaaS products via white-label API.
Best for: Teams on MongoDB, Elasticsearch, or mixed NoSQL/SQL stacks. Healthcare and finance companies needing on-prem AI. SaaS platforms embedding analytics into their product.
ThoughtSpot
ThoughtSpot built its reputation as the NLQ pioneer in BI, among the first major platforms to let business users search data in plain English. Spotter, its current AI agent, extends this with conversational follow-up queries, automated trend detection, and proactive push of key changes to subscribed users.
The platform requires a semantic layer (built in ThoughtSpot Modeling Language) before NLQ or agents can operate. This means a meaningful upfront modeling investment for new deployments, typically weeks to months depending on data complexity. The payoff is deep, consistent AI answers once the layer is built. Data must live in a cloud warehouse (Snowflake, BigQuery, Redshift, Databricks); there is no native connectivity to NoSQL or REST APIs.
Best for: Enterprise teams with large, well-modeled data warehouses and the resources to build and maintain a semantic layer. Strongest when the data estate is already in Snowflake or BigQuery.
Power BI with Copilot
Power BI Copilot is the most widely deployed AI BI feature in the market by install base, given Microsoft’s existing enterprise footprint. Copilot generates report summaries, suggests visualizations, answers natural language questions, and can draft DAX formulas. It functions as an AI assistant on top of existing datasets rather than executing independent multi-step workflows. It requires Microsoft Fabric or a Premium Per User license, not available on standard Power BI Pro.
The AI is powered by Azure OpenAI, meaning queries are processed through Microsoft’s cloud infrastructure. This satisfies many compliance requirements through Microsoft’s data processing agreements, but does not support on-premises Private AI for organizations that cannot send data to any external service. Data must be in a Power BI dataset or Fabric lakehouse; there is no native NoSQL or REST API connectivity without custom connectors and ETL.
Best for: Organizations already in the Microsoft 365 and Azure ecosystem. Teams that need AI-assisted reporting with governance features already built into Microsoft’s compliance stack.
Tableau Pulse
Tableau Pulse is Salesforce’s proactive analytics layer, introduced as a companion to the core Tableau product. It monitors key metrics defined by data stewards and delivers personalized daily digests to business users, surfacing whether a metric is trending up, down, or anomalous and explaining the likely driver. Tableau AI (Einstein) handles natural language follow-ups within the same interface, functioning as an AI assistant on top of published data sources rather than an autonomous workflow executor.
Pulse is more proactive than most tools on this list in that it pushes insights rather than waiting for questions. However, it operates on top of pre-built Tableau published data sources, so the underlying data must already be in Tableau’s semantic layer. SQL-based sources via Tableau’s connectors are well-supported; direct NoSQL access is not available. Tighter Salesforce CRM integration is a relevant advantage for sales and revenue teams.
Best for: Salesforce-centric organizations. Revenue operations and sales analytics teams that need proactive metric monitoring delivered to business users without them opening a BI tool.
Sigma Computing
Sigma is a cloud-native BI platform built on a spreadsheet-style interface that connects directly to Snowflake, BigQuery, Redshift, and Databricks. Ask Sigma, its AI capability, triggers multi-step agentic workflows: it locates relevant data sources, constructs analyses, and surfaces explanations of its reasoning. The architecture is modern and the AI is deeper than most BI tool AI features, though it operates exclusively on cloud warehouse data.
The platform has grown quickly with technical and data-savvy business users who want spreadsheet flexibility with warehouse-scale data. AI capabilities in Sigma are newer than in ThoughtSpot or Power BI but developing rapidly. There is no on-premises deployment option, and no native connectivity outside of cloud warehouses and their supported connectors.
Best for: Data-forward teams on Snowflake or BigQuery that want a modern UI with agentic workflow capabilities. Strong fit for companies replacing Excel with something that can scale to warehouse-size datasets.
Domo AI
Domo is an all-in-one business intelligence and data platform targeting mid-market companies. Domo AI layers an AI assistant, automated reporting, and anomaly alerts onto Domo’s existing dashboard and ETL infrastructure. The AI can answer questions about data, generate chart summaries, and trigger alert workflows when metrics fall outside set thresholds.
Domo’s strength is breadth: it combines data integration, transformation, dashboards, and AI in a single subscription. The agentic capabilities are narrower than platforms built around agents from the start, but the consolidated toolset reduces vendor count for mid-market teams. Data must be loaded into Domo’s cloud platform; direct querying of external NoSQL sources is not supported without ETL.
Best for: Mid-market companies wanting a single-platform approach to data integration and BI. Teams that need out-of-the-box alerts and automated reporting without building custom pipelines.
Tellius
Tellius is an augmented analytics platform focused on automated insight discovery. Its Polaris NLQ engine answers natural language questions, and its AI layer runs automated analysis to surface anomalies, drivers, and trends without requiring a user to know what to look for. Tellius is positioned as the tool that answers “why did this metric change?” without manual slice-and-dice.
The platform sits on top of cloud data warehouses and BI semantic layers rather than replacing them. It excels at root cause analysis and business user-facing insight delivery. Like most tools in this list, it does not support native NoSQL connectivity and requires data to be in a structured warehouse format first.
Best for: Business teams that already have a warehouse and need automated insight discovery on top of it. Particularly strong for finance, marketing, and operations teams focused on root cause analysis at scale.
Want AI agents handling your analytics? Start free – no data team required. Or request a demo for embedded, NoSQL and multi-source use cases.
Agentic BI Tools Comparison
| Platform | AI Architecture | Proactive Insights | NoSQL Native | Private AI / On-Prem | Embedding | Best For |
|---|---|---|---|---|---|---|
| Knowi | Own AI engine, agents in data layer | Yes, agent-initiated | Yes (MongoDB, ES, Cassandra, more) | Yes, full on-prem option | Yes, white-label with multi-tenant | NoSQL/API stacks, healthcare, embedded SaaS |
| ThoughtSpot | AI on semantic layer (Spotter) | Partial, push alerts available | No, warehouse only | No | Partial, embedded SpotIQ available | Enterprise with modeled Snowflake/BigQuery data |
| Power BI + Copilot | Azure OpenAI bolt-on, requires Fabric/PPU | Partial, report summaries only | No, requires ETL to Power BI datasets | No | Yes, complex licensing model | Microsoft/Azure-centric organizations |
| Tableau Pulse | Einstein AI on published data sources | Yes, daily metric digests | No, Tableau connectors only | No | Partial, Tableau Embedded Analytics | Salesforce orgs, revenue/sales analytics |
| Sigma Computing | AI-triggered multi-step workflows (Ask Sigma) | Partial, developing rapidly | No, cloud warehouse only | No | Limited embedding capability | Snowflake/BigQuery teams wanting spreadsheet UI |
| Domo AI | AI assistant on Domo platform data | Yes, alerts and scheduled reports | No, ETL into Domo cloud required | No | Limited via Domo Everywhere | Mid-market all-in-one platform buyers |
| Tellius | Augmented analytics, automated insight discovery | Yes, anomaly and driver detection | No, warehouse-based | No | No native embedding | Business teams doing root cause analysis |
How to Choose an Agentic BI Tool
Four questions narrow the field quickly. First, where does your data live? Teams on MongoDB, Elasticsearch, or REST APIs need a tool with native connectivity – most of the platforms on this list require ETL into a warehouse before AI can touch the data. Second, do you need Private AI? If data cannot leave your environment due to HIPAA, financial regulations, or data residency requirements, the list shortens to two options.
Third, are you embedding analytics into a product? White-label, multi-tenant embedding with row-level security is a specialized capability not uniformly available across these platforms. Fourth, what is the expected setup time? Platforms requiring semantic layer modeling (ThoughtSpot, Looker, to some extent Power BI) deliver consistent AI results but take weeks to stand up. Platforms that query data directly can be operational in days.
For a deeper look at what agentic BI is and how the underlying architecture differs from traditional self-service analytics, that context is useful before committing to an evaluation process. Teams that have already tried traditional BI and found it too slow or too dependent on data engineers tend to evaluate agentic tools more quickly because the problem is clearly defined.
Want AI agents handling your analytics? Start free – no data team required. Or request a demo for embedded, NoSQL and multi-source use cases.
Frequently Asked Questions
What is the difference between agentic BI and AI-assisted BI?
AI-assisted BI adds AI features (natural language questions, chart summaries, suggested visualizations) on top of existing dashboards. Agentic BI uses AI agents that execute multi-step workflows, surface insights proactively, and retain context across queries. The distinction is whether the AI waits to be asked or acts on its own.
Which agentic BI tool works best with MongoDB and Elasticsearch?
Most agentic BI tools require data to be extracted into a SQL warehouse before AI can query it. Native MongoDB and Elasticsearch querying without ETL is available in a small number of platforms. Knowi is built specifically for NoSQL-native analytics and supports MongoDB, Elasticsearch, Cassandra, InfluxDB, and DynamoDB without requiring a data pipeline.
Can agentic BI tools run on-premises?
Most agentic BI platforms are cloud-only. On-premises deployment with Private AI (no data sent to external LLMs) is available in fewer than half the major platforms. This matters most for healthcare organizations with HIPAA requirements, financial services firms with data residency rules, and enterprises with strict data governance policies.
How many AI agents does a typical agentic BI platform have?
The number varies significantly. Some platforms have a single AI assistant that handles all tasks. Others have purpose-built agents for specific jobs: a query agent that writes and executes queries, a dashboard agent that builds and modifies dashboards, a widget agent for individual visualizations, and anomaly detection agents that monitor metrics. Specialized agents generally produce more accurate outputs than single-agent systems on complex tasks.
Do agentic BI tools require a data team to set up?
It depends on the platform architecture. Tools built on semantic layers (ThoughtSpot, Looker, to some extent Power BI) require data modeling work before AI can operate correctly. Platforms that query source databases directly can be set up by a technical founder or product manager without a dedicated data engineering team. The tradeoff is between governance depth and speed of deployment.
Can agentic BI be embedded in a SaaS product?
Yes, though not all platforms support white-label embedding. Embedded agentic BI means your end users get AI-powered analytics inside your product without knowing the underlying vendor. This requires white-label support, multi-tenant row-level security, and an API or SDK for integration. Verify embedding capability specifically during any proof of concept, as the feature set for embedded deployments often differs from the standalone product.
Is Power BI Copilot included in a standard Power BI Pro license?
No. Power BI Copilot requires either Microsoft Fabric capacity or a Power BI Premium Per User license. Standard Power BI Pro accounts do not include Copilot. Organizations evaluating Power BI as an agentic BI tool should confirm licensing costs for Copilot separately, as they are substantially higher than a base Pro subscription.