Unlocking the Power of Agentic AI with Apache Iceberg and Dremio

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Agentic AI is quickly moving from the whiteboard to production. These aren’t just smarter chatbots—they’re intelligent systems that reason, learn, and act with autonomy. They summarize research, manage operations, and even coordinate complex workflows. But while models have become more capable, they still hit a wall without the right data infrastructure.

That wall? It’s not just about storage—it’s about access, performance, and context.

Many organizations building AI agents find themselves struggling with data silos, unpredictable performance, and a lack of clarity around what the data actually means. The result? Agents that stall, generate shallow results, or make the wrong decisions altogether.

To unlock the full potential of Agentic AI, we need to rethink how our data platforms are designed. This is where Apache Iceberg and Dremio come in. Together, they provide a modern, open lakehouse architecture that solves the three core bottlenecks to AI success:

Frictionless access to enterprise data (without data wrangling or replication)
Autonomous, high-performance query acceleration (built for dynamic workloads)
A semantic layer that gives agents the context they need to understand and act

In this post, we’ll break down each of these challenges—and show how Iceberg and Dremio together build the intelligent data backbone your AI agents need to thrive.

The 3 Bottlenecks Blocking Agentic AI from Delivering Real Impact

As promising as Agentic AI is, most organizations hit the same three roadblocks on the path to real-world success. These aren’t just technical hurdles—they’re architectural challenges that undermine the speed, accuracy, and reliability of intelligent agents.

Let’s break them down:

1. Access to Data: Silos, Bottlenecks, and Delays

AI agents need a holistic view of your enterprise to operate effectively—marketing data, operational logs, customer records, product telemetry, and more. But in most environments, that data is scattered across:

Cloud storage systems
Operational databases
SaaS platforms
Departmental data warehouses

Each of these systems may have different governance rules, inconsistent formats, or delayed ETL pipelines. Worse, getting access often requires waiting on central data teams or replicating data manually. This slows down experimentation and limits what your agents can “see.”

2. Performant Access: When Every Millisecond Counts

Even when agents can access data, they still need it fast. AI workflows—especially agentic ones—are dynamic and unpredictable. One minute it’s a lookup query; the next it’s a multi-join aggregation across several sources. Traditional performance tuning—manual partitioning, index maintenance, and query tuning—can’t keep up.

Agents can’t wait minutes for answers. They need sub-second response times to chain actions together effectively. Without autonomous performance management, latency becomes a dealbreaker.

3. Semantic Meaning: Knowing What the Data Actually Means

Access and speed are critical—but so is understanding. AI agents need context to interpret data correctly. What does customer_type = 2 actually mean? Is “margin” defined the same way in marketing and finance? Without a shared semantic layer, agents operate on guesswork.

This is where many AI initiatives fail quietly. Outputs look correct on the surface but are misaligned with how the business actually thinks about its data.

Solving these challenges requires more than patchwork fixes. It demands a new kind of data architecture—one that is open, intelligent, and built for automation. And that’s where Apache Iceberg and Dremio make all the difference.

Apache Iceberg: The Open Foundation for AI-Ready Data

When it comes to building a scalable, AI-optimized data platform, Apache Iceberg is the backbone that holds it all together. It’s not just another table format—it’s the evolution of how data is organized, versioned, and accessed in modern analytics and AI environments.

Think of Iceberg like the index in a giant filing cabinet. It doesn’t just store your data—it brings order, consistency, and flexibility to your data lake, making it feel like a fully featured data warehouse without giving up the openness of object storage.

Why Apache Iceberg Matters for Agentic AI

Agentic AI requires access to data that is:

Consistent: So the same query always returns the same answer.
Evolvable: So schema changes don’t break downstream pipelines.
Portable: So any tool—Spark, Flink, Dremio, or even your AI agents—can access it without vendor lock-in.

Apache Iceberg delivers all of this with features like:

Schema evolution: Add, drop, rename columns without rewriting data.
Time travel: Query data “as of” any point in time, ideal for audits or AI state comparisons.
Hidden partitioning: Optimize performance without complicating your SQL.
ACID transactions: Ensure atomic, consistent updates in multi-writer environments.

By standardizing on Iceberg, your organization can avoid the “tool wars” between departments. Everyone works from the same data foundation, using the tools they prefer—whether it’s SQL notebooks, BI dashboards, or LLM-powered agents.

The Lakehouse Advantage

Iceberg unlocks the full potential of the lakehouse model: combining the flexibility of a data lake with the performance and structure of a data warehouse. This modular approach means:

Teams aren’t forced to centralize around one compute engine.
You avoid redundant data copies and ETL pipelines.
AI agents can query directly from the lakehouse with open standards.

In short, Apache Iceberg makes your data open, unified, and production-grade—everything your intelligent agents need to act with confidence.

Dremio: The Intelligent Data Interface for Agentic AI

Apache Iceberg gives you the open foundation—but Dremio turns that foundation into an intelligent, AI-ready platform. Think of Dremio as the control plane that gives both humans and AI agents seamless access to the data they need, with speed, security, and semantic understanding built in.

Let’s explore how Dremio removes the remaining friction across access, performance, and context.

Unified Access Across All Data (Federation + Simplified Governance)

Even in the best-case scenario, not all your data will live in Iceberg tables. You still have data in relational databases, SaaS tools, cloud data warehouses, and more.

This is where Dremio’s Zero-ETL Federation shines. Dremio connects directly to all your sources—whether it’s Amazon S3, PostgreSQL, Salesforce, or MongoDB—and lets you query them in place, without copying data or building fragile pipelines.

Benefits for Agentic AI:

Agents can query the full landscape of enterprise data through a single interface.
Centralized access control means fewer credentials to manage or expose.
Real-time insights from operational systems without waiting on ingestion jobs.

Autonomous Performance for Unpredictable Workloads

Agentic AI is dynamic by nature—queries change based on real-time decisions. You can’t rely on hand-tuned optimizations or static dashboards.

Dremio solves this with autonomous performance management, including:

Automatic Iceberg Table Optimization: Dremio continuously compacts small files, sorts data, and maintains metadata health to reduce I/O and boost query speed.
Reflections: Dremio’s version of intelligent materialized views. They’re automatically created, updated incrementally, and substituted at query time—so your agents get faster results without changing their SQL.
Multi-layered Caching: From query plans to result sets to object storage blocks, Dremio caches intelligently to accelerate repeat workloads and reduce cloud costs.

This means whether your AI agent is summarizing a dashboard or crunching through user logs, it gets fast, consistent results—without human intervention.

Built-in Semantic Layer for Shared Understanding

To generate meaningful insights, agents need to understand not just what data is, but what it means. Dremio provides a native semantic layer that bridges that gap.

Semantic Search: Agents and users can discover datasets using natural language.
Data Modeling: Define reusable business logic, KPIs, and metrics as views.
Auto-Generated Wikis: Every dataset can include human-readable descriptions—great for onboarding both analysts and AI systems.
Fine-Grained Access Control: Row- and column-level security ensures agents see only what they’re authorized to.

And with Dremio’s MCP server, your agents can programmatically explore metadata, access semantic context, and generate more accurate queries.

Dremio doesn’t just connect to your data—it understands it, optimizes it, and makes it consumable by anyone (or anything) that needs it. For Agentic AI, this is the difference between guesswork and precision.

Closing the Loop: Iceberg + Dremio = AI-Optimized Lakehouse

When you bring Apache Iceberg and Dremio together, you don’t just get a modern data stack—you get a foundation built for the realities of Agentic AI.

Let’s recap how these technologies align to eliminate the core bottlenecks we explored earlier:

✅ Unlocking Access

Apache Iceberg standardizes how data is stored, making it accessible across tools and teams.
Dremio federates access to all your data sources—cloud, on-prem, SaaS, and more—without the overhead of ETL or manual integration.
AI agents can now query the full enterprise landscape through a single interface, using a single set of credentials, securely and efficiently.

✅ Delivering Performance Autonomously

Iceberg enables high-performance table management (partitioning, file pruning, metadata tracking).
Dremio automates this further—handling compaction, caching, and query acceleration behind the scenes.
Reflections, smart caching, and autonomous query optimization ensure agents get sub-second responses, no matter how complex or spontaneous the query.

✅ Embedding Context Through Semantics

Iceberg brings structure to your data lake, but Dremio gives it meaning.
Through Dremio’s built-in semantic layer and MCP server, your AI agents can interpret, navigate, and reason about data the way your business does.
Whether it’s knowing what “active customer” means or filtering by business unit, Dremio gives your agents the vocabulary to deliver trusted outcomes.

The result is a truly intelligent lakehouse—open, unified, performant, and semantically rich. One that doesn’t just serve humans, but empowers agents to act, adapt, and deliver real business value.

If Agentic AI is your destination, Apache Iceberg and Dremio are the road and the vehicle that will take you there.

Get hands-on with Dremio and Apache Iceberg today and start building the intelligent data foundation your AI agents need to thrive.