Training course · AI Agent Development · Internal Tools & Automation

AI Agent Development for Internal Tools and Automation

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 Internal Tools and Automation” is designed for roles such as Software developers maintaining internal applications and scripts, DevOps, SRE and platform teams and IT administrators with automation responsibilities and shows step by step how to use AI Agent Development productively, safely and transparently in Internal Tools & Automation – 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.

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 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.
  • 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 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: 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

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. 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

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

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.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Salzburg Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Online Request
  • Thu, 7 January 2027 – Fri, 8 January 2027 Bremen Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Augsburg 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.

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 “AI Agent Development 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

More AI Agent Development courses

All 30 AI Agent Development courses

AI Agent Development · MunichAI Agent Development · SalzburgAI Agent Development · BerlinAI Agent Development · AugsburgAI Agent Development · HanoverAI Agent Development · BremenAI Agent Development · StuttgartAI Agent Development · KaiserslauternAI Agent Development · Online