Organizations cannot implement AI quickly when data is fragmented across systems, ungoverned, and lacks the business context AI needs to deliver accurate results. Teams juggle schema knowledge, joins, query tuning, visualization tools, and governance checks before they can answer even a simple business question. With Dremio's Agentic Lakehouse—the only data platform built for agents and managed by agents—we remove that friction by bringing a natural-language agent directly into the lakehouse experience.
We're excited to announce Dremio's Lakehouse AI Agent, a conversational interface that accelerates business insight and decision-making. In a single interface, using natural language, you can ask questions, generate insights, and drive actions—all backed by unified, governed data and the AI Semantic Layer that provides the business context AI needs for accurate analysis.
By changing how your organization makes decisions through natural language and instant visualizations, you shorten business cycles from weeks to minutes. Let's explore how Dremio is accelerating the process of questions → insights → actions.
Key highlights:
An AI agent for data analysis is an intelligent system that interprets natural language questions, navigates enterprise data autonomously, and delivers accurate insights through visualizations and recommendations—all while respecting governance policies and maintaining business context. Unlike traditional BI tools that require users to know SQL or understand data schemas, a Lakehouse AI agent acts as a conversational interface between business professionals and their data, translating intent into optimized queries that leverage semantic definitions and performance optimizations automatically.
Dremio's Lakehouse AI Agent stands apart by operating directly on unified, governed data within the Agentic Lakehouse. The agent leverages the AI Semantic Layer to understand business terminology, relationships between datasets, and metric definitions—ensuring that answers are not just fast, but accurate and trustworthy. This enables real-time analytics that delivers insights in seconds rather than hours, empowering business professionals to make data-driven decisions without waiting for data engineering resources or navigating complex technical barriers.
Explore this interactive demo and see how Dremio's Intelligent Lakehouse enables Agentic AI
The evolution from data warehouses to AI data lakehouses represents a fundamental shift in how organizations store, govern, and analyze data for AI workloads. Traditional data warehouses were designed for structured data and known queries, requiring expensive data copies and rigid schemas that slow AI adoption. The data lakehouse combines the flexibility of a data lake with the governance and performance of a warehouse—but Dremio's Agentic Lakehouse goes further by embedding AI capabilities directly into the platform, enabling agents to operate on unified, governed data without pipelines or duplication.
| Aspect | Traditional Data Warehouse | AI Data Lakehouse |
|---|---|---|
| Data structure | Structured data only, requires ETL for unstructured content | Handles structured, semi-structured, and unstructured data natively with AI Functions for processing |
| Schema approach | Schema-on-write requires upfront modeling, slowing iteration | Schema-on-read with semantic layer provides flexibility while maintaining governance and business context |
| Use cases | Historical reporting, known queries, BI dashboards | Real-time analytics, exploratory analysis, AI workloads, agentic analytics without predefined queries |
| Scalability and cost | Expensive compute tied to storage, vendor lock-in with proprietary formats | Open architecture (Iceberg, Polaris, Arrow) with autonomous scaling delivers 20× performance at lowest cost |
| AI readiness | Requires separate AI infrastructure, data copies, and pipelines for AI workloads | Native AI execution with AI Semantic Layer, AI Functions, and agent integration—delivering fastest path to AI insights |
The Agentic Lakehouse eliminates the operational burden of traditional architectures through Autonomous Reflections and Automatic Iceberg Clustering, which optimize performance automatically—freeing platform teams from manual tuning while delivering sub-second query performance at a fraction of the cost of warehouses or cloud platforms.
In many enterprises, analytics require translating business questions into SQL, understanding schemas, navigating joins and aggregations, and managing governance checks. The handoff to data specialists creates dependencies and slows down the decision-making process, often taking days or weeks to answer questions that should take minutes.
Using the Dremio AI Agent for data analysis eliminates this barrier. You can ask questions in natural language—"What are our top-performing products by region this quarter?" or "How do customer acquisition costs trend across channels?"—and let the system handle intent, context, and nuance. Questions can be simple or complex, specific or exploratory, standalone or part of an investigative thread. The agent maintains context across the entire conversation, allowing questions to build naturally on each other as your understanding deepens.
The AI Semantic Layer ensures that the agent understands business terminology, metric definitions, and data relationships—translating your questions into optimized SQL that respects governance policies and leverages Autonomous Reflections for sub-second performance. This means business professionals get accurate answers backed by governed data, without requiring SQL expertise or understanding technical schemas.
Raw answers aren't enough—understanding requires visualization and context. Dremio's AI Agent transforms questions directly into visual representations of real-time data, backed by high-quality analysis and insights. These visualizations become part of an ongoing analytical conversation, where each view can be refined, expanded, or pivoted based on what you need insight into.
Using Dremio's AI Agent for data visualization helps you move from a question to an answer in seconds, and it accelerates exploration and experimentation, leading to greater business value. You can pursue more paths, test more hypotheses, and discover insights that would never surface through traditional reporting cycles. The agent doesn't just answer questions—it reveals patterns, highlights anomalies, and surfaces connections that you might not think to look for.
Because visualizations are generated from unified, governed data, you can trust that insights reflect accurate, up-to-date information from across your entire data landscape. The AI Semantic Layer ensures consistent interpretations, eliminating the confusion that arises when different teams analyze the same concepts using different definitions or data sources.
Insights without action create no value. Dremio's AI Lakehouse Agent completes the cycle by enabling immediate action on discoveries. You can export visualizations for presentations, create alerts for critical metrics, share insights with stakeholders, or trigger workflows based on findings—all within the same conversational interface.
The agent helps you move from "what happened" to "what should we do" by providing actionable recommendations based on your data patterns. The Lakehouse AI solution suggests next steps, highlights risks requiring attention, and identifies opportunities for optimization. This seamless transition from insight to action ensures that discoveries translate into business impact.
Every action maintains full lineage tracking, showing exactly which data sources contributed to insights and which governance policies were applied. This auditability enables confident decision-making in regulated environments, where understanding data provenance is essential for compliance and risk management.
When your questions-to-insights-to-actions cycle is compressed from weeks to minutes, something fundamental changes in how you operate. Dremio's Agentic Lakehouse delivers three transformative benefits that accelerate business value while reducing cost and operational complexity.
You can explore more options, validate more assumptions, and test more strategies. This isn't just doing the same things faster—it's doing things that were previously impossible due to time constraints. When analysis takes seconds instead of days, you can pursue exploratory questions, investigate edge cases, and discover insights that would never surface in traditional reporting cycles.
How enterprises are impacted:
The acceleration compounds: each quick answer enables another question, creating a rapid learning loop that builds competitive advantage through faster, more informed decision-making.
With rapid iteration, you can dig deeper into root causes, explore edge cases, and understand nuance. Decisions become based on a comprehensive understanding rather than surface-level analysis. The AI Semantic Layer ensures that every insight reflects accurate business context, eliminating the inconsistencies and misinterpretations that plague traditional analytics.
How enterprises are impacted:
Financial planning teams can validate assumptions across multiple data sources instantly, ensuring that forecasts reflect complete, accurate information rather than fragmented snapshots.
Product managers can understand customer behavior at granular levels, identifying subtle patterns that inform better feature prioritization and roadmap decisions.
Risk management teams can investigate anomalies from multiple angles, distinguishing genuine risks from statistical noise before escalating to stakeholders.
Customer success teams can correlate satisfaction scores with product usage patterns, support interactions, and business outcomes—revealing intervention opportunities that prevent churn.
Decisions backed by comprehensive analysis and governed data create trust throughout the organization, enabling bolder strategic moves with confidence in the underlying insights.
Each cycle teaches something new, and when cycles happen quickly, learning compounds. You and your teams develop sharper intuition about what questions to ask, what patterns matter, and which actions drive results. The conversational interface removes barriers between business professionals and data, democratizing analytics while maintaining governance.
How enterprises are impacted:
This accelerated learning creates a compounding advantage: teams that can answer questions faster, make better decisions, and learn more quickly pull ahead of competitors still relying on traditional analytics workflows.
Dremio’s agentic AI for data analysis also serves as your educational and investigative partner. With a single click in the Dremio Console, you now have access to personalized guidance for your SQL queries and jobs. The interactive chat interface means you're not limited to a single answer as you evaluate your SQL and job performance. Ask follow-up questions, explore alternatives, and dive deeper into any topic.
Your Lakehouse AI Agent maintains context throughout the conversation, providing refined guidance as you work through challenges. This educational capability accelerates team development while reducing the burden on platform teams—business professionals become more self-sufficient, data engineers gain optimization insights, and everyone benefits from faster resolution of technical challenges.
Have you ever analyzed a complex query and spent hours attempting to decipher what it does? Simply click "Explain SQL" and receive:
No more guessing or lengthy documentation searches—get instant insight on even the most complex SQL. Ask follow-up questions like "How can I make this faster?" or "What would happen if I added this filter?" The agent provides contextualized guidance that helps you understand not just what the query does, but why it's structured that way and how to improve it.
Wonder why your query takes longer than expected? Click "Explain Job" for comprehensive insights:
Transform execution plans into clear optimization steps, discover patterns you weren't aware of, and drive efficiency without manual tuning. The agent helps platform teams maintain optimal performance while enabling business users to understand how their queries consume resources—creating shared understanding that improves both query authoring and infrastructure planning.
Dremio's Lakehouse AI Agent represents more than a new feature—it's a new relationship between you and your data. Instead of your data being locked behind technical barriers, it becomes conversationally accessible. Instead of insights requiring specialized skills, they become easily discoverable. Instead of waiting for specialized analysis, you make decisions at the speed of business—all backed by unified, governed data that ensures accuracy and compliance.
This transformation doesn't require replacing existing systems or retraining your entire organization. It simply requires starting a conversation. The same data that currently sits in reports and digital dashboards becomes dynamically explorable through natural dialogue. The same decisions that currently take weeks can be made in minutes, without sacrificing accuracy or governance.
Consider how implementing a data-driven AI agent for ROI improvement changes common business scenarios:
Each scenario demonstrates how the Agentic Lakehouse accelerates ROI by eliminating barriers between questions and answers. The AI Semantic Layer ensures accuracy, unified data eliminates silos, and conversational interaction democratizes insights—enabling organizations to operate faster, decide better, and compete more effectively.
The enterprise data stored in your systems already contains the insights that could transform your business. Your team already has the questions that could unlock those insights. Dremio's Agentic Lakehouse—the only data platform built for agents and managed by agents—simply connects them through natural conversation backed by unified, governed data.
The transformation begins with a single question. That question leads to an insight, which drives an action, which raises the next question. Each cycle builds capability, confidence, and competitive advantage. With the AI Semantic Layer providing business context, Autonomous Reflections delivering performance automatically, and open standards eliminating vendor lock-in, Dremio enables the fastest path to AI at the lowest cost—without operational burden.
In the Dremio Agentic Lakehouse, experience the power of conversational analytics with Dremio's AI Agent. Transform your questions into insights into actions—and transform your business in the process.
Book a demo today and see how Dremio's Agentic Lakehouse and AI Agent can accelerate decision-making, improve ROI, and drive results for your business—without pipelines, lock-in, or operational overhead.
An AI Lakehouse helps control AI costs through three fundamental mechanisms that eliminate the expensive infrastructure and operational overhead of traditional AI implementations.
First, autonomous operations reduce compute consumption. Autonomous Reflections automatically optimize query performance without manual tuning, while Automatic Iceberg Clustering optimizes file layouts continuously—delivering 20× performance at a fraction of the cost of traditional data warehouses or cloud platforms. This means organizations pay only for consumption with industry-leading price-performance, avoiding the expensive over-provisioning required by systems that lack autonomous optimization.
Second, zero-copy architecture eliminates data duplication costs. Traditional AI implementations require copying data into separate systems for processing, creating storage costs, data movement expenses, and ETL pipeline maintenance overhead. Dremio's federated query architecture and AI Functions enable AI workloads to operate on data where it lives, eliminating costly pipelines and reducing total cost of ownership.
Third, unified governance prevents compliance penalties and security incidents. Organizations that implement AI without proper governance face risks of data breaches, regulatory fines, and compliance violations—costs that far exceed infrastructure expenses. Dremio's fine-grained access controls, lineage tracking, and unified governance ensure that AI agents operate within security boundaries, maintaining compliance while processing sensitive data at scale.
Lakehouse AI enables five primary use cases that deliver immediate business value across industries:
Executive decision support enables leadership teams to explore strategic questions conversationally during meetings, receiving instant insights backed by governed data from across the organization. Rather than waiting days or weeks for analyst reports, executives can investigate market opportunities, evaluate operational performance, and validate assumptions in real-time—accelerating strategic decision-making without sacrificing accuracy.
Self-service analytics for business teams democratizes data access by letting sales, marketing, operations, and finance professionals explore data through natural language without SQL expertise or data engineering support. This reduces bottlenecks on platform teams while empowering business professionals to make data-driven decisions independently, scaling analytics adoption without increasing operational overhead.
Operational monitoring and anomaly detection enables teams to monitor business metrics conversationally, asking questions like "Are there unusual patterns in today's transaction volumes?" or "What's driving the spike in support tickets?" The agent identifies anomalies, correlates them with potential causes, and recommends actions—all through natural conversation that maintains context across investigations.
Cross-functional collaboration and analysis breaks down data silos by enabling teams from different departments to explore shared data conversationally. Marketing can analyze how campaigns affect support ticket volumes, sales can correlate pricing changes with customer satisfaction, and operations can connect supply chain metrics with financial performance—all through unified, governed data that ensures consistent interpretations.
Platform optimization and cost management helps data teams monitor lakehouse operations conversationally, identifying optimization opportunities, understanding resource consumption patterns, and receiving recommendations for Autonomous Reflections or Automatic Iceberg Clustering configurations—reducing manual tuning while maintaining optimal performance at the lowest cost.
Successfully implementing an AI agent for data visualization requires attention to five critical areas that ensure accurate, trustworthy, and performant analytics:
Build a semantic layer first. The AI Semantic Layer provides the business context AI agents need to generate accurate visualizations and interpretations. Define business metrics, establish relationships between datasets, document data definitions, and create consistent terminology before enabling conversational analytics. This foundation ensures agents understand what data means in business terms, eliminating misinterpretations and inconsistent analysis.
Maintain unified, governed data. AI agents can only deliver trustworthy insights when operating on unified data with consistent governance. Implement fine-grained access controls that respect user permissions, establish lineage tracking for audit and compliance, and federate queries across sources to eliminate data silos—ensuring every visualization reflects accurate, governed information from a single source of truth.
Start with high-value use cases. Begin implementation with specific use cases where conversational analytics delivers clear ROI—executive decision support, self-service analytics for specific teams, or operational monitoring. Demonstrate value quickly, build confidence in AI-generated insights, and expand gradually based on user feedback and observed patterns of successful adoption.
Enable iterative exploration. Design conversational flows that support follow-up questions, refinement of visualizations, and exploratory analysis. The most valuable insights emerge through conversation, where initial visualizations spark new questions that lead to deeper understanding. Ensure agents maintain context across conversations, enabling natural progression from high-level overviews to detailed investigation.
Leverage autonomous performance optimization. Implement Autonomous Reflections and Automatic Iceberg Clustering to ensure AI agents deliver sub-second performance without manual tuning. Fast response times are essential for conversational analytics—delays disrupt the natural flow of exploration and reduce user engagement. Autonomous optimization maintains performance as data volumes grow and query patterns evolve, without increasing platform team workload.
Deploy agentic analytics directly on Apache Iceberg data with no pipelines and no added overhead.
By unifying data from diverse sources, simplifying data operations, and providing powerful tools for data management, Dremio stands out as a comprehensive solution for modern data needs. Whether you are a data engineer, business analyst, or data scientist, harnessing the combined power of Dremio and Apache Iceberg will undoubtedly be a valuable asset in your data management toolkit.
This blog helps you learn about table-driven access policies in Dremio Cloud and Dremio Software v24.1+.
Dremio Arctic bring new features to Dremio Cloud, including Apache Iceberg table optimization and Data as Code.
