Unlocking Enterprise Intelligence with Agentic RAG, Apache Iceberg, and Dynamiq on watsonx.data

The next chapter in enterprise AI is driven by agents that can seamlessly connect to structured data warehouses and unstructured knowledge bases, blending diverse data types. These agents not only retrieve information but also reason and act, dynamically adapting to complex workflows.

This marks a major shift from traditional retrieval-augmented generation (RAG) systems to Agentic RAG systems—where access to various data types, dynamic decision-making, and continuous feedback loops are fully integrated.

Dynamiq’s agentic RAG platform, integrated with IBM watsonx.data and its components such as Apache Iceberg and Milvus, helps build AI agents that empower users to extract insights from both structured and unstructured data sources. Its intuitive visual builder, integrated testing suite, and flexible deployment options (cloud or on-premises) significantly shorten the time from pilot to production.

The Agentic RAG Revolution

Agentic RAG advances beyond traditional RAG by introducing autonomous AI agents that:

• Dynamically route queries across structured SQL databases and vector stores containing unstructured data.
• Self-optimize by employing reasoning and acting principles, using continuous feedback loops to analyze both tabular and textual data.
• Maintain detailed audit trails that track decision-making processes across traces.

Why Agentic RAG Matters

As enterprises scale their AI initiatives, simply retrieving information is no longer enough. Traditional RAG systems focus on search and retrieval, but today’s business environments demand more dynamic, context-aware systems that can adapt to real-time data and complex workflows.

Agentic RAG systems bring three critical advantages

Smarter Decision-Making

By reasoning and acting on retrieved data, agents can provide context-rich insights and recommend next steps, not just answers.

Greater Efficiency

Automated workflows reduce manual intervention, speeding up processes like compliance audits, HR queries, customer support, and operational reporting.

Stronger Governance and Transparency

Detailed audit trails and real-time monitoring ensure organizations maintain visibility, compliance, and control over how AI systems operate—critical for regulated industries.

In short, Agentic RAG enables enterprises to move beyond basic information retrieval toward dynamic, intelligent decision support systems that scale with their business needs.

Architectural Breakdown

1. Structured Data Foundation with Apache Iceberg

IBM watsonx.data uses Iceberg as its core table format, enabling:

• Time-aware queries to compare current and historical transaction patterns.
• Schema evolution to adapt to new compliance requirements without rewriting data.
• Hybrid cloud storage through S3-compatible object storage synchronization.

2. Unstructured Processing Pipeline

Dynamiq integrates seamlessly with watsonx.data’s native tools to deliver:

• Document vectorization using IBM’s Granite embedding models.
• Storage of vectorized data in Milvus, part of watsonx.data.
• Version-aware retrieval mechanisms.

3. Agentic Orchestration Layer

Dynamiq’s agentic AI platform includes a visual workflow builder that enables:

• Multi-agent collaboration between SQL analysts and semantic search agents.
• Query routing based on LLM-powered decisions.
• Compliance guardrails that detect PII leakage.

The resulting Agentic RAG architecture is a hybrid approach combining Iceberg tables with vector search.

Example of Enterprise Implementation: Internal HR Systems

Use Case: Employees and Internal Policies

Challenge: Managing structured employee data alongside unstructured HR documents (e.g., PTO policies, vacation allowances, employee stock programs).

Solution:

• Iceberg Data Lake
  
  • Store transaction records.
  • Enable ACID-compliant updates.
  • Standardize data formats and schemas.

• Vector Knowledge Base
  
  • Embed internal policies using watsonx.data’s native models.
  • Maintain hybrid Milvus indexes.
• Agentic Workflow
  
  • Built within minutes using the Dynamiq platform.

Results:

• Over 90% accuracy in answering user questions.
• Response times within seconds through concurrent processing of Iceberg tables and vector queries.
• Comprehensive audit trails capturing LLM reasoning flows and actions applied to data sources.

Advantages of Agentic RAG with Apache Iceberg and Dynamiq on watsonx.data

• Faster Compliance Audits
  
  • Detailed traces for every step performed by Reasoning and Acting (ReAct) AI agents.
  • Unified Dynamiq logs across all involved technologies.

• Lower Storage Costs
  
  • Iceberg’s ZSTD compression combined with Milvus quantization.
  • Tiered storage policies via watsonx.data.

• Zero-Shot Adaptation
  
  • Agents automatically handle new policies by querying dynamic data stores.
  • Schema evolution without pipeline downtime.

The Future of Enterprise RAG

Emerging capabilities will further transform AI agent-based systems:

• Auto-RAG Optimization
  
  • Dynamiq selection of LLMs to power agents.
  • Cost-aware query planning.

• Improved Version Control
  
  • Coordinated snapshots across Iceberg, Milvus, and agent layers.
  • Git-like branching for experimental workflows and enhanced version testing.

By integrating Iceberg’s structured intelligence with agentic RAG, businesses can build AI systems that not only answer questions but also understand the full context of enterprise operations. These systems dynamically adapt to evolving workflows and leverage real-time data retrieval, advanced reasoning, and continuous feedback loops. This adaptability ensures enterprises meet the demands of complex environments without sacrificing governance or transparency.

Many organizations are already piloting these architectures, addressing the challenge of moving from pilot to production.

Early adopters use visual builders to rapidly prototype and test agentic workflows, reducing production timelines from months to days—or even from weeks to hours. As production deployments grow, the understanding and practical use of agentic AI advances, bringing us closer to the era of truly intelligent enterprise AI.