Training course · AI Agents · Quality Management

AI Agents for Quality Management Teams

Agentic AI for business teams: understand AI agents, automate workflows safely and keep control with human-in-the-loop 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 AI Agents with the everyday processes of Quality Management. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

AI agents go a step beyond conventional chat assistants: they plan sub-tasks, use tools such as search, files or business applications and complete multi-step work largely on their own. Over two days you will learn how agents are built, how they differ from chatbots and traditional automation, and which tasks genuinely justify their use. Widely used platforms serve as practical examples, including Microsoft Copilot Studio, custom GPTs and Claude with projects and tool connections.

In the hands-on sessions you design agent workflows for tasks from your own area of work, from triaging requests through research and drafting to handing results over to business systems. You decide where a person reviews and approves, which permissions an agent receives and how errors are detected. A dedicated block covers governance, data protection and accountability, so that your agents not only work but remain controllable, traceable and defensible within your organisation.

Quality management depends on clean documentation, systematic analysis and rigorous follow-up of actions. Generative AI can relieve quality teams exactly there: AI assistants create and revise procedures, prepare audits, analyse complaints, support 8D reports and FMEA and compare documents with standard requirements such as ISO 9001 or IATF 16949. Automation sends reminders about action deadlines, evaluates inspection data and consolidates supplier ratings. That leaves more time for root cause work, process improvement and collaboration with production and engineering. AI output must not replace conformity with standards; it needs expert review and must enter the management system through controlled processes. When document control and approval workflows are respected, AI becomes a reliable tool for quality and efficiency.

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?

  • Quality managers and management representatives
  • Quality engineers and quality planners
  • Internal auditors
  • Complaint and supplier quality managers
  • Process owners in management systems

Typical challenges we address

  • Keeping extensive quality documentation current, consistent and compliant with standards
  • Analysing and resolving complaints and nonconformities quickly
  • Preparing internal and external audits efficiently
  • Reliably tracking actions from audits, 8D reports and management reviews

What you will be able to do

  • You can explain how AI agents are structured and clearly distinguish them from chatbots and classic automation.
  • You can select a suitable agent platform for your requirements using transparent criteria.
  • You can design an agent workflow for a real process in your area and build a working prototype.
  • You can define human-in-the-loop checkpoints, test cases and escalation rules.
  • Create and maintain quality documentation efficiently and in line with standards using AI.
  • Use AI for complaint analysis, 8D, FMEA and audit preparation.
  • Integrate AI output into your management system in a controlled and traceable way.

Course outline

Day 1

Module 1 What makes an AI agent
  • Agent building blocks: language model, instructions, knowledge, tools and memory
  • How agents differ from chatbots, macros and classic workflow automation
  • Levels of autonomy: from suggestion-making assistants to agents that act independently
  • Realistically judging which tasks suit agents and which do not
Module 2 Platform overview: Copilot Studio, GPTs and Claude
  • Microsoft Copilot Studio: agents grounded in company data and connected to Microsoft 365
  • Custom GPTs: configuring instructions, knowledge files and actions
  • Claude with projects, knowledge bases and tool access via the Model Context Protocol
  • Selection criteria: data access, integrations, administration and licensing
Module 3 Designing and building agent workflows
  • Breaking tasks into steps and defining hand-offs between agent and human
  • Writing system instructions, examples and knowledge sources for consistent output
  • Connecting tools: search, documents, email, spreadsheets and business applications
  • Building a prototype agent for a real process from your own area
Module 4 Human-in-the-loop, quality and error handling
  • Setting approval steps, escalation rules and thresholds for human review
  • Test cases, evaluation criteria and spot checks for quality assurance
  • Common failure modes: wrong tool choice, endless loops and fabricated facts
  • Using structured user feedback to improve the agent over time

Day 2

Module 5 Governance, security and roll-out
  • Roles and responsibilities across business units, IT, data protection and works council
  • Least-privilege permissions, logging and protection against prompt injection
  • Positioning under the EU AI Act and GDPR: transparency, documentation and data flows
  • From pilot agent to production: operation, maintenance and measuring success
Module 6 Practice lab: Quality Management
  • Writing procedures
  • Standards gap analysis
  • 8D report support
  • FMEA preparation
  • Audit preparation
  • Complaint data analysis
Module 7 Data protection, quality & transfer to daily work
  • AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.
  • 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

Writing procedures

An AI assistant turns process descriptions and interviews into procedures and work instructions with a consistent structure, checks them for contradictions with other documents and suggests improvements.

Standards gap analysis

AI compares existing documents with the requirements of ISO 9001, IATF 16949 or ISO 13485, highlights possible gaps and produces an action list for expert evaluation.

8D report support

For complaints, AI structures the problem description, suggests containment actions and possible root causes and writes the 8D report in a form customers can understand.

FMEA preparation

Based on process steps and experience, AI suggests failure modes, effects and causes and helps the team facilitate and document FMEA sessions.

Audit preparation

From standard clauses and the process map, AI creates audit questions, checklists and audit plans and condenses audit notes into a structured draft report with findings.

Complaint data analysis

Complaints and inspection results are analysed by defect type, product and supplier, trends identified and priorities for improvement projects proposed.

Prerequisites

You should already use an AI assistant such as ChatGPT, Copilot or Claude on a regular basis. No programming skills are required. You work in quality management and know the requirements of your management system, for example ISO 9001.

Technical requirements

You will need a laptop with an up-to-date browser. Ideally you have access to at least one agent platform such as Copilot Studio, ChatGPT with GPTs or Claude; practice accounts can be provided if needed.

Data protection & compliance

AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Wed, 2 December 2026 – Thu, 3 December 2026 Munich Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Berlin Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Online Request

Frequently asked questions

Do I need programming skills to build AI agents?

No. The platforms used on the course rely on visual interfaces, plain-language instructions and ready-made connectors. You learn to specify agents precisely from a business perspective, connect tools and test workflows. Where deeper technical integration would be needed, you will know which requirements to put to your IT team.

Which platform will we use during the course?

The course is vendor-neutral and uses Microsoft Copilot Studio, custom GPTs and Claude as examples. You build your prototype on the platform that is available or planned in your organisation. The principles of workflow design, control and governance you learn carry over to other agent solutions as well.

How do I stop an agent from acting without control?

Through deliberate design: agents receive only the minimum permissions they need, critical actions such as sending, booking or deleting require human approval, and every step is logged. In addition, you define test cases and escalation rules. These are exactly the control mechanisms you develop for your own use case during the course.

Do auditors accept AI-generated documents?

What matters is not who drafted a document but whether it is technically correct, controlled, approved and applied in practice. If you review AI drafts, release them through your document control and keep responsibility clear, they meet the standard's requirements. We show how to integrate AI cleanly into these processes.

Can AI replace an FMEA?

No. An FMEA relies on the team's cross-functional knowledge of product and process. AI can prepare, make suggestions and document, which makes sessions more efficient. Ratings and decisions on actions remain with the team, which is accountable for the result.

How much does “AI Agents for Quality Management Teams” 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 Quality Management Teams

More AI Agents courses

All 45 AI Agents courses

AI Agents · MunichAI Agents · SalzburgAI Agents · BerlinAI Agents · AugsburgAI Agents · HanoverAI Agents · BremenAI Agents · StuttgartAI Agents · KaiserslauternAI Agents · Online