Training course · Agentic Coding · Internal Tools & Automation
Agentic Coding for Internal Tools and Automation
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 Internal Tools & Automation. 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.
Almost every company has a long backlog of small tools nobody gets round to building: admin interfaces for master data, scripts for recurring data reconciliation, Slack or Teams bots, report generators and glue between SaaS systems. With AI coding assistants and agents, internal tools like these can be built in hours rather than weeks. Developers, ops and platform teams therefore shift their focus from writing code to clear requirements, secure access and reliable operation. Internal tools in particular often hold far-reaching rights over production data and quickly become business-critical. In this course you learn to build internal tools and automations rapidly with AI, implement authentication and permissions correctly from the start and design solutions that stay documented, tested and maintainable.
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?
- Software developers maintaining internal applications and scripts
- DevOps, SRE and platform teams
- IT administrators with automation responsibilities
- Operations and RevOps engineers
- Business process automation teams
Typical challenges we address
- A long backlog of internal tools that never get prioritised next to product work
- Manual reconciliation between SaaS systems, spreadsheets and databases that is prone to error
- Internal tools with excessive permissions, no logging and no clear owner
- Undocumented scripts that only one person understands
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.
- Build internal tools, bots and automations with AI agents quickly and to a solid standard.
- Implement authentication, permissions, logging and secrets management correctly from the outset.
- Design automations that are documented, testable and operable, so they do not depend on individual people.
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: Internal Tools & Automation
- Generating admin interfaces
- Automating data reconciliation
- Chat bots for teams
- Automating ops runbooks
- Generating reports and exports
- Building agent workflows
Module 7 Data protection, quality & transfer to daily work
- Internal tools often access employee, customer and production data: you learn to grant permissions on a least-privilege basis, keep secrets in vaults rather than code, log access and take GDPR and works council requirements into account for tools that touch employee data.
- 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
Generating admin interfaces
Create CRUD interfaces from a database schema or API with search, filters, validation, role-based permissions and an audit log, so business teams can maintain master data safely.
Automating data reconciliation
Write scripts that regularly pull data from CRM, ERP, ticketing systems and spreadsheets, reconcile it and report discrepancies, with retry logic, idempotency and clear error messages.
Chat bots for teams
Build Slack or Teams bots that answer status queries, trigger approvals, announce deployments or provide on-call information while only executing explicitly permitted actions. Every action is logged with the user ID.
Automating ops runbooks
Turn recurring operational tasks such as user management, certificate renewal or log analysis into reviewed scripts and workflows, safeguarded with a dry-run mode and logging.
Generating reports and exports
Automatically prepare regular analyses from databases and APIs as PDF, spreadsheet or email, including scheduling, parameters and traceable data sources. Manual copy-and-paste between systems disappears entirely.
Building agent workflows
Connect AI agents to tools such as the ticketing system, knowledge base and internal APIs, for example via MCP, to pre-qualify requests, gather data and prepare drafts for human approval.
Possible hands-on projects
Onboarding automation
A workflow that creates accounts in several systems for new starters, assigns groups and sends the line manager a checklist of outstanding steps.
Licence overview
An internal dashboard that pulls usage data from SaaS tools, flags unused licences and shows costs per team at a glance.
Deployment bot
A chat bot that announces deployments, summarises changes from pull requests and notifies the right people when a pipeline fails.
Prerequisites
You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. You have programming experience in a language such as Python, TypeScript or Go and know the basics of APIs, databases and the command line.
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
Internal tools often access employee, customer and production data: you learn to grant permissions on a least-privilege basis, keep secrets in vaults rather than code, log access and take GDPR and works council requirements into account for tools that touch employee data.
Suggested dates
All dates are suggestions – your preferred date is always possible.
- Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
- Mon, 30 November 2026 – Tue, 1 December 2026 Kaiserslautern Request
- Mon, 7 December 2026 – Tue, 8 December 2026 Online Request
- Wed, 13 January 2027 – Thu, 14 January 2027 Augsburg Request
- Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
- Wed, 3 February 2027 – Thu, 4 February 2027 Salzburg 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.
When is a custom internal tool better than an off-the-shelf product?
When a process is specific to your organisation, spans several systems or standard tools only fit after heavy customisation. AI cuts development cost considerably, but operation and maintenance remain. In the course you learn to weigh effort against benefit realistically and build tools that keep working with minimal upkeep.
How do we secure internal tools that were built quickly with AI?
With fixed guardrails: sign-in through your central identity provider, role-based permissions, no secrets in code, logging of every write action and a short review before go-live. These building blocks can be stored as a template so AI agents include them automatically in every new tool. You create exactly these templates in the course.
How much does “Agentic Coding for Internal Tools and Automation” 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.
More courses for Internal Tools and Automation
- Claude Code for Internal Tools and Automation
- Vibe Coding for Internal Tools and Automation
- Cursor for Internal Tools and Automation
- GitHub Copilot for Internal Tools and Automation
- OpenAI Codex for Internal Tools and Automation
- Windsurf for Internal Tools and Automation
- Lovable for Internal Tools and Automation
- Bolt for Internal Tools and Automation
- Replit Agent for Internal Tools and Automation
- Model Context Protocol (MCP) for Internal Tools and Automation
- AI Agent Development for Internal Tools and Automation
- RAG & LLM Applications for Internal Tools and Automation
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