Build with AI: SQL AI Agents in Production

Course details

Make SQL agents product-ready by learning how to leverage Python, LangChain, and LLM frameworks. First, learn about the overall architecture of SQL AI agents by focusing on building a robust framework that uses deterministic information. Find out how to create effective prompt templates, add context, and inject memory into agents to handle complex and dynamic queries with ease. Dive into safety assurance, query validations, and safety constraints. Review monitoring systems and error-handling process. Then, set up logs to track the agent performance with MLflow. Last but not least, see how to implement multiple AI agents to process the data.

This course is designed for data scientists and engineers but is also beneficial to data analysts seeking to increase knowledge and build operational skills in AI systems. By the end of the course, you will have learned how to prototype SQL AI agents and deploy them responsibly into production environments.

Concepts

Introduction

Build a SQL AI agent for production

SQL AI Agent Architecture and Python Settings

Building production-ready SQL AI agents - Course overview
Setting up the course environment
Prepare and ingest data

Build Context Rich SQL AI Agents

Designing prompt templates for SQL AI agents
Building SQL AI agents with LangChain Chains
Resolving ambiguity with deterministic context
Inject deterministic context into an SQL agent prompts
Add conversational memory to an SQL AI agent
Use skills to inject domain knowledge into your SQL agent

Implementing Multiple Query Validation Layers

Identifying risks - What could go wrong with an AI agent
Query validation layers
Implementing a query validation layer
Setting SQL safety constraints

Error Handling and Fallback Method

Handling invalid SQL syntax - Creating an error handler
Debugger agent architecture for SQL AI systems
Enabling fallback options - Defining a fallback model system
Building and integrating a testing framework

Monitoring and Logs

Observability for SQL AI agents
Designing a logging system
Designing a monitoring dashboard for SQL AI agents
Alerting strategies for SQL AI agents