Training course · Agentic Coding · SQL & Databases

Agentic Coding for SQL and Database Professionals

Use autonomous coding agents effectively: specs instead of prompts, task decomposition, test-driven loops, multi-agent setups and governance.

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

This course connects Agentic Coding with the everyday processes of SQL & Databases. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Agentic coding means working with AI agents that carry out development tasks largely on their own: they read code, plan changes, run commands and tests, and correct themselves based on the results. Tools such as Claude Code, OpenAI Codex, the GitHub Copilot coding agent and Cursor’s agent mode have made this way of working practical for everyday use. This vendor-neutral course teaches the underlying methodology so that you achieve reliable results whatever the tool, and redefine the developer’s role within your engineering team.

The core shift is from ad hoc prompts to robust specifications. You will learn to decompose and describe tasks so that agents solve them reliably, to set up test-driven loops in which tests act as objective acceptance criteria, and to run several agents in parallel or with a division of labour. We also cover review strategies for agent-generated code and team governance: permissions, security boundaries, accountability, cost control and the gradual introduction of agentic workflows into your existing development processes.

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 understand how coding agents work and choose the right tool and level of autonomy.
  • You write specifications and project context that agents can work with reliably.
  • You break initiatives into verifiable sub-tasks and steer agents through test-driven loops.
  • You use multi-agent setups and parallel work in a controlled way.
  • 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 Foundations of agentic development
  • How coding agents work: tool calls, context windows and feedback loops
  • Comparing tools: terminal agents, IDE agents and cloud agents
  • Recognising suitable tasks and choosing levels of autonomy deliberately
  • The new role in the team: from implementation to steering and acceptance
Module 2 Specifications instead of prompts
  • Describing tasks with goal, context, constraints and acceptance criteria
  • Keeping project context in files such as CLAUDE.md, AGENTS.md or rules
  • Plan-first approach: let the agent draft a solution and review it before implementation
  • Versioning specifications and maintaining them as part of the repository
Module 3 Task decomposition and test-driven agent loops
  • Breaking large initiatives into independent, verifiable sub-tasks
  • Having tests written first and using them as an objective stopping criterion
  • Using linters, type checks and builds as automatic feedback
  • Recognising stuck loops and restarting with a new strategy
Module 4 Multi-agent setups and parallel work
  • Defining specialised subagents for research, implementation, testing and review
  • Organising parallel work with branches, worktrees and isolated environments
  • Orchestration: merging results and resolving conflicts
  • Sharing tools and data sources through MCP

Day 2

Module 5 Review, security and team governance
  • Review strategies for large, agent-generated changes
  • Defining permissions, sandboxing and protection against prompt injection
  • Embedding agents in CI pipelines and pull request processes
  • Planning team guidelines, accountability, costs and a phased rollout
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

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. You write SQL regularly, understand joins, aggregations and indexes and work with at least one relational database system.

Technical requirements

Your own laptop with Git, a terminal and your development environment, plus access to at least one coding agent such as Claude Code, Codex, Copilot or Cursor.

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.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Berlin Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Stuttgart Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Hanover Request

Frequently asked questions

How does agentic coding differ from vibe coding?

Vibe coding relies on fast, intuitive iteration in natural language and is particularly suited to prototypes. Agentic coding is an engineering discipline for professional codebases: precise specifications, tests as acceptance criteria, clear permissions and structured reviews ensure that agents deliver reliable, maintainable results even in complex, long-lived projects.

Is the course tied to a specific tool?

No. The course teaches methods that work with Claude Code, OpenAI Codex, the GitHub Copilot coding agent, Cursor and comparable tools. The exercises highlight differences and common ground, for example in context files, permissions and subagents. You can work with whichever tool your organisation already uses or plans to adopt.

How do we stay in control of quality and security with autonomous agents?

Control comes from several layers: tightly scoped permissions and sandboxing, tests and static analysis as automatic checks, mandatory human review before merging, and clear team rules on accountability. During the course you develop a governance concept that fits your processes, security requirements and regulatory obligations.

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 “Agentic Coding 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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