Training course · AI Agent Development · SQL & Databases

AI Agent Development for SQL and Database Professionals

Build AI agents professionally: architectures, tool use, agent SDKs, LangGraph, memory, evals, guardrails and deployment.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“AI Agent Development for SQL and Database Professionals” is designed for roles such as Database and SQL developers, Database administrators for PostgreSQL, SQL Server, Oracle or MySQL and Backend developers responsible for data models and ORM layers and shows step by step how to use AI Agent Development productively, safely and transparently in SQL & Databases – from the fundamentals to advanced workflows.

AI agents go beyond conventional chatbots: they break tasks down, call tools and APIs, evaluate the results and keep iterating until a goal is met. In this two-day course you learn to build such systems so that they work reliably, transparently and safely in day-to-day business. You compare architectural patterns ranging from simple workflows through the agent loop to multi-agent systems, and you implement them hands-on with the Claude Agent SDK, the OpenAI Agents SDK and LangGraph.

The focus is not on a quick demo but on the path to production. You design tool interfaces and context deliberately, connect tools via the Model Context Protocol, plan short-term and long-term memory and build evaluations that surface regressions early. On top of that come guardrails against prompt injection and harmful actions, human-in-the-loop approvals, tracing, cost control and deployment. By the end you can judge when an agent is worthwhile, when a deterministic workflow is enough and which framework fits your project.

Databases are the memory of every application, and their performance, consistency and security determine whether entire systems succeed. AI coding assistants and agents support database developers and DBAs in writing complex queries, reading execution plans, designing schemas and index strategies and migrating between database platforms. They translate business questions into SQL, refactor sprawling stored procedures and generate test data. One principle still holds: a syntactically valid query is not necessarily a correct one, and an agent with write access to production data is a serious risk. In this course you learn to use AI productively with schema context, execution plans and tests, and to design access so that your data stays protected at all times.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Database and SQL developers
  • Database administrators for PostgreSQL, SQL Server, Oracle or MySQL
  • Backend developers responsible for data models and ORM layers
  • Data engineers and analytics engineers
  • BI developers working with complex reporting queries

Typical challenges we address

  • Slow queries and load spikes whose causes are hidden in execution plans
  • Sprawling stored procedures and triggers without documentation or tests
  • Risky schema changes and migrations on live systems
  • Different SQL dialects during platform changes or in multi-database estates

What you will be able to do

  • You choose the right pattern for a task, from a fixed workflow to an autonomous agent.
  • You design tools and context so that agents work reliably and cost-effectively.
  • You implement agents with the Claude Agent SDK, the OpenAI Agents SDK or LangGraph.
  • You set up evaluations and tracing to measure quality and detect regressions.
  • Optimise queries and indexes with AI support based on execution plans rather than guesswork.
  • Plan and test schema changes and database migrations using generated scripts and rollback strategies.
  • Design database access for AI agents securely, with least privilege, read replicas and traceable queries.

Course outline

Day 1

Module 1 Agent architectures and design patterns
  • Workflows versus agents: prompt chaining, routing, parallelisation, orchestrator-worker
  • The agent loop: plan, act, observe, reflect
  • Multi-agent systems, subagents and handoffs between agents
  • Criteria for choosing model, framework and level of autonomy
Module 2 Tool use and context engineering
  • Function calling and structured outputs with JSON Schema
  • Designing good tools: granularity, naming, error messages, idempotency
  • Connecting and reusing tools via MCP servers
  • Managing the context window: summarisation, compaction, retrieval and prompt caching
Module 3 Frameworks in practice
  • Claude Agent SDK: agent loop, tools, subagents, permissions and hooks
  • OpenAI Agents SDK: agents, handoffs, guardrails and tracing
  • LangGraph: stateful graphs, checkpointing and human-in-the-loop
  • Side-by-side comparison on the same task and criteria for choosing a framework
Module 4 Memory, evals and observability
  • Short-term and long-term memory, storage strategies and forgetting
  • Evaluation datasets, LLM-as-a-judge and task-based success metrics
  • Tracing tool calls, token usage and latency
  • Regression testing when prompts, models or tools change

Day 2

Module 5 Guardrails, security and deployment
  • Protecting against prompt injection, data exfiltration and over-privileged tools
  • Input and output checks, approval steps and sandboxing
  • Deploying as a service, asynchronous execution, retries and cost limits
  • Governance, audit logging and requirements under GDPR and the EU AI Act
Module 6 Practice lab: SQL & Databases
  • Optimising SQL queries
  • Designing and reviewing schemas
  • Refactoring stored procedures
  • Planning migrations safely
  • Translating dialects
  • Generating test data
Module 7 Data protection, quality & transfer to daily work
  • Databases often hold personal and business-critical data: you learn to give AI tools schemas and anonymised samples instead of production data, manage credentials securely and implement GDPR requirements such as data minimisation and deletion policies.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Optimising SQL queries

Have execution plans from EXPLAIN ANALYZE or Query Store interpreted, identify bottlenecks such as full table scans or poor join orders and get rewritten queries and suitable indexes with a clear rationale.

Designing and reviewing schemas

Derive normalised data models with keys, constraints and data types from requirements, review existing schemas for redundancy and missing integrity rules and generate ER diagrams.

Refactoring stored procedures

Explain large PL/SQL or T-SQL procedures, convert cursor loops into set-based logic, standardise error handling and add tests with frameworks such as tSQLt or pgTAP.

Planning migrations safely

Generate versioned migration scripts for Flyway or Liquibase, design zero-downtime strategies such as expand and contract and assess rollback paths as well as effects on locking and replication.

Translating dialects

Migrate queries, functions and data types between Oracle, SQL Server, PostgreSQL and MySQL, account for differences in date functions, sequences and transaction behaviour and test for identical results.

Generating test data

Create realistic synthetic datasets that respect constraints, distributions and edge cases without using personal production data, for load tests and development environments. Generators live in the repository under version control.

Possible hands-on projects

Slow query analyser

A script that processes the slow query log or statistics views, groups the most expensive queries and reports concrete index and rewrite suggestions.

Schema documentation generator

A tool that reads a database schema and produces searchable documentation with table descriptions, relationships and an ER diagram. It refreshes automatically after each migration.

Text-to-SQL assistant

A small web app with read-only access to a reporting database that turns business questions into checked SQL and shows both query and result transparently.

Prerequisites

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. You write SQL regularly, understand joins, aggregations and indexes and work with at least one relational database system.

Technical requirements

A laptop with Python or Node.js, Git and a code editor, plus API access to at least one LLM provider such as Anthropic or OpenAI. Credentials for the exercises can be provided if needed.

Data protection & compliance

Databases often hold personal and business-critical data: you learn to give AI tools schemas and anonymised samples instead of production data, manage credentials securely and implement GDPR requirements such as data minimisation and deletion policies.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Munich Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Online Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Stuttgart Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Berlin Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Online Request

Frequently asked questions

Which agent framework should we use?

It depends on your model strategy, the level of control you need and your team’s skills. Vendor SDKs offer a lean start with tightly integrated features, while LangGraph gives you finer control over state and flow. In the course you solve the same task with several frameworks and derive clear selection criteria for your own project.

How do you stop an agent from taking wrong or dangerous actions?

With several layers of protection: narrowly scoped tools with minimal permissions, input and output validation, approval steps for critical actions, sandboxing and continuous evaluation. Prompt injection via external content is treated as a realistic threat. You practise these measures directly on your own agent.

Does every automation need an agent?

No. Many tasks are handled more reliably, cheaply and transparently by a fixed workflow of a few LLM calls. Agents pay off when the solution path cannot be planned in advance and the system must react flexibly to intermediate results. We train exactly this trade-off using practical examples.

Should an AI agent access our database directly?

It can, but it needs careful planning. We recommend a dedicated read-only user, access to replicas or development databases rather than production, query timeouts and logging of every statement. Write changes go through versioned migration scripts with review. In the course you build exactly this safeguard, for instance through a restricted MCP server.

How reliable is AI-generated SQL?

It is usually syntactically correct, but semantic quality depends heavily on context: without the schema, relationships and business definitions, wrong joins or double counting creep in easily. With schema context, example queries and tests against known results, reliability rises considerably. You learn to validate generated queries systematically.

How much does “AI Agent Development for SQL and Database Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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