GH-600: Development in Agent-Based AI Systems

Price
Net: €
VAT: €

Price
Price on Request

Duration
1 day

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
Online Live

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More and more applications are no longer limited to working with individual prompts; instead, they independently perform tasks, make decisions, and coordinate multiple tools simultaneously. Agentic AI combines large language models with planning, contextual understanding, and tool calling. This results in AI agents that are capable of significantly more than traditional chatbots.

Key Topics

Architecture of Modern Agentic AI Solutions
Putting GitHub Models to Work
Prompt engineering for complex tasks
Model Context Protocol (MCP)
Development of Intelligent Agents
Multi-Agent Concepts and Orchestration
API Integrations and External Tools
Security, Testing, and Best Practices

Prerequisites
Experience in software development and a basic understanding of GitHub and APIs are helpful. Prior exposure to AI or cloud technologies will aid in understanding the material.

Target Audience
Developers, DevOps teams, cloud engineers, AI specialists, and technical professionals who want to develop modern agent systems for businesses.

The combination of LLMs, GitHub Models, and intelligent agents opens up new possibilities for automation, assistance systems, and business applications. Hands-on expertise facilitates the creation of powerful AI solutions for a wide range of use cases.

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Course Content

Development of Agent-Based AI Systems – 1 of 2

Fundamentals of Agent-Based AI on GitHub
Agent Architecture Design and Integration into the Software Development Life Cycle (SDLC)
Tools, MCP, and Execution Environments for Agents

Development of Agent-Based AI Systems – Part 2 of 2

Multi-Agent Systems and Orchestration
Memory, State, and Evaluation
Control, Safety Measures, and Operation

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently Asked Questions

For which roles are Agentic AI Systems particularly relevant?

Agentic AI is primarily used in software development, AI development, automation, and product development. AI engineers, software developers, solution architects, ML engineers, and technical project managers benefit in particular. Teams that develop intelligent applications with autonomous decision-making processes also gain valuable expertise through this.

What prior knowledge makes it easier to get started with agent-based AI?

A basic understanding of Python, APIs, and modern AI models provides a solid foundation. Experience with large language models, machine learning, or cloud platforms makes it even easier to get started. Those who already develop software or integrate AI applications can apply new concepts to real-world projects more quickly.

What tasks do Agentic AI Systems handle in day-to-day business operations?

Agentic AI Systems automate complex processes, make decisions based on defined goals, and independently coordinate multiple steps. Typical applications include intelligent assistants, workflow automation, customer service, data analysis, software development, and the management of multi-step business processes.

Why are multi-agent systems considered an important future trend?

Multi-agent systems enable multiple specialized AI agents to collaborate. This allows complex tasks to be efficiently divided among them, results to be cross-checked, and processes to be scaled. This architecture is particularly well-suited for enterprise applications, research, automation, and agent-based software solutions.

Which technologies play a key role in the development of agent-based AI?

The focus is on large language models (LLMs), APIs, retrieval-augmented generation (RAG), function calling, vector databases, agent frameworks, and cloud and container technologies. The combination of these components enables intelligent applications that retrieve information, use tools, and perform actions autonomously.

What challenges commonly arise with Agentic AI?

The biggest challenges are reliable decision-making logic, data security, cost control, and the traceability of AI actions. Clear roles, defined goals, guardrails, monitoring, and systematic testing ensure that agents operate in a stable, secure, and reproducible manner.

In which industries are concrete applications already emerging?

Agentic AI is already being used in IT, finance, manufacturing, healthcare, retail, and public administration. Typical applications include automated support processes, intelligent document processing, software development, process automation, and AI-powered analytics and decision-making systems for complex business processes.

What professional value does expertise in Agentic AI provide?

Expertise in agent-based AI is among the most in-demand future skills in the field of artificial intelligence. Companies are looking for professionals who can develop, integrate, and responsibly operate autonomous AI systems. This expertise opens up career opportunities in software development, AI engineering, cloud computing, and digital transformation.

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