Build an agentic text-to-SQL RAG system
Stop building a new dashboard for every business question. Design an agentic RAG system that turns plain English into safe SQL, charts, and explanations, with intent routing, query-correction loops, and the production guardrails real users need.
What You Will Learn
- ✓Build a natural language to SQL pipeline that handles real queries
- ✓Design a multi-agent architecture with LangGraph for complex data tasks
- ✓Add guardrails that prevent dangerous queries from reaching your database
- ✓Auto-generate Plotly visualizations from query results
- ✓Ship a Chainlit chat UI your team can actually use
Who Is This For
- AI engineers who who want to build practical multi-agent systems beyond chatbots
- Data engineers who tired of writing ad-hoc SQL for every stakeholder question
- Full-stack developers who who want to add AI-powered data exploration to their products
Curriculum
Introduction to agentic RAG
3 lessons
Database knowledge base
3 lessons
Agent state & foundations
3 lessons
Core agent implementations
5 lessons
Visualization pipeline
3 lessons
LangGraph workflow orchestration
3 lessons
Chainlit UI integration
3 lessons
Production project
4 lessons
Frequently Asked Questions
- Do I need to know LangGraph?
- No. The workshop teaches LangGraph from the ground up. You should be comfortable with Python and basic LLM concepts.
- What database does this use?
- PostgreSQL via Docker. The workshop includes a sample dataset so you do not need your own data to follow along.
- Can I use this with my own database?
- Yes. The architecture is designed to be database-agnostic. Swap the connection string and schema, and the agent adapts.
- Is this for beginners?
- This is an advanced workshop. You should have completed the RAG Fundamentals and Building AI Agents courses first, or have equivalent experience.
Build an agentic text-to-SQL RAG system
Stop building a new dashboard for every business question. Design an agentic RAG system that turns plain English into safe SQL, charts, and explanations, with intent routing, query-correction loops, and the production guardrails real users need.
Chosen by engineers automating data pipelines at scale
The Ad-hoc data nightmare. Sound familiar?
Dashboard explosion
You've built 50 metabase dashboards, but the marketing team still asks you to pull 'just one more custom metric' every Friday.
Dangerous SQL generation
You tried using LangChain's SQL agent, but it hallucinates non-existent columns or tries to DROP tables when confused.
Contextless numbers
You send stakeholders a spreadsheet of raw SQL results, and they immediately email back asking what the numbers actually mean.
The data bottleneck
Your highly-paid data engineers spend 40% of their week writing basic SELECT statements for business users.
The fix isn't a better prompt.
It's a multi-agent guardrail architecture.
Senior engineers aren't better because they know more syntax. They're better because they've:
Using LangGraph, we decouple generation, execution, and validation into a state machine you can actually trust.
Not a toy demo.
A production data interface.
We don't stop at generating a basic SELECT statement. You'll build a complete Chainlit UI that handles chart rendering, error recovery, and complex multi-step analytical reasoning.
The agentic build path
From raw schema to an autonomous data analyst.
Schema injection
You build dynamic context loaders so the LLM perfectly understands your database structure.
The core nodes
You create the isolated actors: SQL Generator, SQL Executor, and the Error Recovery Loop.
LangGraph wiring
You connect the nodes into a state machine that can conditionally route queries and retry failures.
UI & visualization
You launch a Chainlit interface that streams thoughts, executes queries, and outputs interactive charts.
From Architecture to a Deployed Agentic System.
Introduction to agentic RAG
Understand the problem, architecture, and set up your development environment
The problem we're Solving
Why traditional chatbots fail and how agentic RAG solves it
Architecture overview
Understand the full agent workflow and each agent role
Environment setup
Set up your development environment and API keys
Database knowledge base
Understand the e-commerce dataset and schema context
Understanding the dataset
Explore the Brazilian e-commerce dataset structure
Schema as Context
How we provide database schema to the LLM
Database initialization
How the SQLite database is created from CSV files
Agent state & foundations
Learn LangGraph fundamentals, state management, and agent personas
LangGraph fundamentals
Understanding StateGraph and graph-based workflow orchestration
Agent state with typeddict
Defining the shared state that flows through all agents
Agent personas & prompts
How each agent is configured with its role and system prompt
Core agent implementations
Build the Guardrails, SQL, Executor, Error, and Analysis agents
Guardrails agent
Validate user intent and filter out-of-scope questions
SQL agent
Generate valid SQL queries from natural language
Executor agent
Execute SQL queries against the database
Error recovery agent
Diagnose and fix SQL errors automatically
Analysis agent
Transform query results into natural language explanations
Visualization pipeline
Build intelligent visualization decision and Plotly chart generation
Visualization decision agent
Decide when and what type of chart to generate
Visualization agent with Plotly
Generate interactive Plotly charts from query results
Plotly integration patterns
Common chart patterns and best practices
LangGraph workflow orchestration
Build the StateGraph that connects all agents together
Building the stategraph
Connect agents into a complete workflow
Conditional routing
Implement decision points with conditional edges
Streaming & debugging
Stream agent events for real-time UI updates
Chainlit UI integration
Build the chat interface with Chainlit
Chainlit fundamentals
Understanding Chainlit decorators and message handling
Building the chat interface
Integrate the agent workflow with Chainlit UI
Interactive charts in Chainlit
Display Plotly visualizations in the chat
Production project
Run, customize, and extend your chatbot
Running your chatbot
Start the application and test it end-to-end
Switching LLM providers
Use different LLMs via LiteLLM
Customization challenges
Ideas for extending your chatbot
Final assessment & celebration
Test your knowledge and celebrate your achievement
Workshop Curriculum
What you'll build in this hands-on workshop.
Amateurs prompt. Engineers architect.
Scripts are easy.
Anyone can ask ChatGPT to write a SQL JOIN.
Production RAG is hard:
- Design cyclic state graphs for error recovery
- Implement rigid guardrails against hallucinated tables
- Architect dynamic schema retrieval for large databases
- Stream complex multi-agent execution states to a UI
Stop building unreliable chat wrappers. Build resilient agent systems.
Learn From Someone Who Actually Does This
I am the Cofounder of AEOsome.com and Chief Mentor at learnwithparam.com, with 15+ years of software engineering experience building production systems.
"Most engineers are not blocked by ability, but by lack of real system ownership."
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Frequently Asked Questions
Do I need to know LangGraph?
What database does this use?
Can I use this with my own database?
Is this for beginners?
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