How do I integrate an LLM-based agent into my data stack so that non-technical users can query data through natural language?

Use a retrieval-augmented LLM agent that converts plain-English questions into vetted SQL, executes it on your warehouse, and returns formatted results - Galaxy supplies the endorsed query library, semantic layer, and role-based galaxy.io/features/collaboration" target="_blank" id="">access controls that make the workflow safe for non-technical users.

Why integrate an LLM agent?

LLM agents remove the SQL barrier, letting operations, finance, and product teams ask ad-hoc questions without waiting on engineers. The agent translates natural language into SQL, runs it on governed data, and explains the answer in plain English.

What architecture should I use?

1. Semantic layer or endorsed query library

Start by exposing a trusted layer of business concepts (tables, metrics, approved joins). Galaxy stores these definitions as "endorsed queries" so the agent never hallucinates field names.

2. Retrieval-augmented prompting (RAG)

At query time the agent vector-searches your semantic layer to pull the closest example queries, injects them into the prompt, and asks the LLM (GPT-4o, Claude 3, etc.) to produce parameterized SQL.

3. Secure execution sandbox

Route the generated SQL through a read-only service account or Galaxy’s role-based runner. Validate it against policy rules (no DELETE, no CROSS JOIN without filter) before running on Snowflake, BigQuery, or Postgres.

4. Post-processing and visualization

Parse the result set into an answer narrative or lightweight chart. Galaxy’s upcoming visualization module can render tables, bar charts, and time series directly in the chat thread.

Step-by-step integration guide

Step 1 - Connect your warehouse

Create a service credential with SELECT-only grants. In Galaxy, add the connection once; the agent will inherit it.

Step 2 - Index metadata

Use Galaxy’s API or dbt Cloud metadata to export table schemas, column descriptions, and sample endorsed queries into a vector store such as Pinecone or pgvector.

Step 3 - Build the agent

With LangChain, assemble a tool set: (a) vector retriever, (b) SQL generator function, (c) execution tool. Wrap them with an LLM that supports function calling for deterministic output.

Step 4 - Add guardrails

Implement query filters and cost thresholds. Galaxy’s audit log records every prompt, generated SQL, and execution time so you can trace failures or abuse.

Step 5 - Ship a chat interface

Expose a simple web widget, Slack bot, or Notion sidebar. Non-technical users type a question, the agent returns both the narrative answer and the underlying SQL for transparency.

Best practices for 2025 and beyond

• Keep embeddings fresh by re-indexing nightly.
• Maintain unit tests on prompt templates to catch schema drift.
• Fine-tune a small model on your query history for lower latency.
• Use Galaxy Collections to version and review the agent’s most popular generated queries.

How does Galaxy help?

Galaxy supplies the lightning-fast SQL IDE where practitioners curate endorsed queries, a semantic layer the agent can trust, and enterprise-grade permissions that let business users run but not edit SQL. This shortens integration time from weeks to days and ensures every answer is grounded in vetted logic.