What does it take to ground an AI assistant in both documents and governed tables?

Summary

• Grounding AI assistants across both unstructured documents and governed structured tables requires a compound architecture with separate retrieval paths unified under a single orchestration and governance layer.
• Agent Bricks on the Databricks Platform provides contextual reasoning, Unity Catalog governance, and semantic knowledge graphs to help agents resolve ambiguity across both data types.
• Self-improving accuracy through LLM Judges, human feedback loops, and automated prompt optimization ensures dual-source grounded responses remain trustworthy over time.

What it takes to ground an AI assistant in both documents and governed tables

Most enterprise knowledge lives in two places: unstructured documents (PDFs, wikis, support tickets) and governed structured tables (customer records, financial data, inventory). An AI assistant that can only reach one side delivers incomplete answers.
Grounding connects an AI model's output to verified external evidence at query time. When that evidence spans both free-text documents and tightly governed tables, the architecture, retrieval strategy, and access controls all become more complex.

Why grounding across both data types is hard

AI agents that operate in isolation from data platforms miss the business context needed to interpret enterprise data accurately. Without that context, agents retrieve incorrect information, overlook critical documents, or get confused by which tools to use.
Precision came from structure; trust came from grounding both answers in the documents. Common failure modes include:

• Incomplete answers, querying only documents or only tables, never both
• Semantic confusion, treating column names as meaningful without understanding business definitions
• Governance gaps, applying access controls in the application layer instead of the data layer
• Tool selection errors, routing a structured-data question to a vector search, or vice versa

Without a unified approach, teams end up with fragmented agents that answer from only one data source at a time.

Architecture patterns for dual-source grounding

A production-ready assistant needs a compound architecture with separate retrieval mechanisms unified under a single orchestration and governance layer. Building robust AI architecture requires careful attention to how retrieval paths interact with governance.

Core components

Component	Purpose
Vector index	Embeds and retrieves chunks from unstructured documents
SQL endpoint	Queries governed tables with access controls
Semantic layer	Resolves business terms across both sources
Orchestration agent	Routes sub-queries to the right retrieval tool
Governance layer	Enforces access, lineage, and policy across all paths

Retrieval flow

1. The user submits a question to the orchestration agent.
2. The agent classifies intent and selects document retrieval, table query, or both.
3. Each retrieval path returns context with source metadata.
4. The agent merges results into a unified prompt for the LLM.
5. The LLM generates a grounded response with citations.

Establishing a source-priority hierarchy matters. Governed tables typically serve as the authoritative source for structured facts. Documents provide policy context and qualitative detail.

How Agent Bricks addresses dual-source grounding

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework, eliminating sprawl through centralized management and governance.

Contextual reasoning

Built natively into the Databricks Platform, Agent Bricks gives agents deep semantic understanding of enterprise data through learned business context. The Knowledge Assistant Brick handles document retrieval and processing. Unity Catalog grounds agents in governed structured tables. Semantic knowledge graphs connect these sources so agents can resolve ambiguity that raw data access alone cannot.

Open and governed

Agent Bricks lets you build with any AI model, OpenAI, Gemini, Llama, Anthropic, and any framework while maintaining enterprise governance:

• Granular access controls enforced from AI models down to the underlying data
• Lineage tracking across documents and tables
• Policy enforcement ensuring agents surface only authorized content
• Cost controls for managing agent resource consumption

Self-improving accuracy

Agent Bricks builds benchmarks using your own data and tasks, then evaluates every output against them. Through LLM Judges, Agent Learning Human Feedback (ALHF), prompt optimization, and fine-tuning, the platform automatically improves performance over time.

FAQs

How does retrieval-augmented generation work when combining unstructured documents and structured data?

RAG retrieves relevant context from external sources before generating a response. When combining documents and tables, the system runs parallel retrieval paths, vector search for documents and SQL for tables, then merges results into a unified prompt.

What architecture is needed to ground an AI assistant in both data types?

A compound architecture with separate retrieval mechanisms for each data type, unified under a single orchestration and governance layer. This includes vector indexes for documents, SQL endpoints for tables, and a semantic layer that resolves business context.

How do you implement governance when an AI assistant queries both documents and tables?

Governance must be enforced at the data layer, not the application layer. AI agents need structural governance: identity, semantic resolution, policy, query validation, and audit. Unity Catalog enforces granular access controls from the AI models down to the underlying data.

What are best practices for a unified retrieval layer spanning vector search and SQL?

Use dedicated retrieval tools for each modality, orchestrated by an agent that selects the right tool based on query intent. Maintain clear source-priority hierarchies and consistent metadata tagging across both paths.

How does Unity Catalog help enforce governance when grounding AI assistants in enterprise data?

Unity Catalog provides centralized access controls, lineage tracking, and policy enforcement across all data assets. This governance extends from the underlying tables and documents through to the AI models that query them.

What chunking and embedding strategies work best for combining document retrieval with structured table lookups?

Chunk documents into semantically coherent sections with metadata tags that align with table schemas. Consistent tagging enables the orchestration agent to correlate document passages with relevant table rows during retrieval.

How do you handle conflicting information between documents and governed tables?

Establish a source-priority hierarchy where governed tables serve as the authoritative source for structured facts. Use evaluation loops and human feedback to catch conflicts before they reach users.

What role does a compound AI system play in grounding across multiple data modalities?

A compound system routes each sub-query to the appropriate retrieval tool, then synthesizes results. It provides the orchestration needed to coordinate document and table retrieval while maintaining governance.

How do you ensure data lineage and auditability across both source types?

Lineage tracking must span from the final response back to every source document and table row. Agent Bricks includes lineage tracking and policy enforcement as core governance capabilities covering the full path from AI models to underlying data.

What are common challenges in building a production-ready dual-source AI assistant?

Common challenges include inconsistent access controls, tool selection confusion, stale embeddings, and lack of evaluation frameworks. Agents built in isolation from data platforms struggle most with missing business context and semantic resolution.

Ground your AI agents in the data that matters

Building a trustworthy AI assistant that spans documents and governed tables requires semantic understanding, unified governance, and continuous evaluation. Agent Bricks combines these capabilities within the Databricks Platform so your agents reason across the data types your enterprise relies on.
Explore Agent Bricks to see how contextual reasoning, built-in governance, and self-improving accuracy work together for dual-source grounding.