Build a Claude Code Verification Harness

Oct 8, Reserve a seat
Free to read

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.

Start readingPreview curriculum

Still deciding? Ask first.

Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.

  • Curriculum fit, prerequisites, or where to start
  • Honest answer, no pressure to enroll

Taught by an engineer who has shipped this

  • ISO 27001

    Led the engineering work behind the certification of a regulated EU platform.

  • Series A platform

    Architected the case-management product that became the business a €11.6M round was raised on.

  • 3x faster deploys

    Cut time-to-deploy by migrating to Kubernetes on GCP with deploy-on-merge.

Build the app every data team wishes they had: a chatbot that converts natural language to SQL, auto-generates Plotly visualizations, and runs on a LangGraph multi-agent architecture, complete with guardrails, error handling, and a Chainlit UI you can deploy today.

Build production agentic AI systems, from architecture to deployment.

What you'll ship

Real projects, not toy demos.

  • Turn plain English questions into SQL queries automatically
  • Coordinate multiple specialized agents to answer complex data questions
  • Add guardrails that stop bad queries before they hit your database
  • Generate charts and visualizations from query results
  • Ship a chat UI your team can actually use to explore data

What you'll learn

You finish able to:

  • 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

Curriculum

From Architecture to a Deployed Agentic System.

  1. 01

    Introduction to agentic RAG

    Understand the problem, architecture, and set up your development environment

  2. 02

    Database knowledge base

    Understand the e-commerce dataset and schema context

  3. 03

    Agent state & foundations

    Learn LangGraph fundamentals, state management, and agent personas

  4. 04

    Core agent implementations

    Build the Guardrails, SQL, Executor, Error, and Analysis agents

  5. 05

    Visualization pipeline

    Build intelligent visualization decision and Plotly chart generation

  6. 06

    LangGraph workflow orchestration

    Build the StateGraph that connects all agents together

  7. 07

    Chainlit UI integration

    Build the chat interface with Chainlit

  8. 08

    Production project

    Run, customize, and extend your chatbot

Who it's for

Is this for you?

AI engineers

who want to build practical multi-agent systems beyond chatbots

Data engineers

tired of writing ad-hoc SQL for every stakeholder question

Full-stack developers

who want to add AI-powered data exploration to their products

FAQ

Common 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.