Reviews 5,0
Reviews 5,0
GH-600: Development in Agent-Based AI Systems
Agentic AI extends traditional software development with systems that plan tasks, deploy tools, and coordinate workflows autonomously. To implement this, AI components must be integrated into existing development processes in a controlled manner.
- Planning Agent Architectures in the SDLC
- Using GitHub Models
- Developing complex prompts
- Integrating MCP and tools
- Orchestrating multi-agent systems
- Accounting for Memory and State
- Implementing controls and security measures
Prerequisites
Experience in software development as well as basic knowledge of GitHub and APIs; some experience with AI or cloud technologies is helpful.
Target Audience
Software development, DevOps, cloud engineering, AI specialization, and technical development roles.
The focus is on the technical implementation of agent-based AI as a component of modern software solutions.
PDF
Course Content
Developing Agent-Based AI – Part 1 of 2
- Agent-Based AI on GitHub
- Agent Architecture and SDLC Integration
- Tools, MCP, and Agent Environments
Developing Agent-Based AI – Part 2 of 2
- Multi-Agent Systems and Orchestration
- Memory, State, and Evaluation
- Control, Security, and Operation
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
For which professional roles is Agentic AI a good fit?
- This topic is particularly relevant for software development and technical roles that integrate AI agents into applications and development processes.
Do I need to have prior experience with AI agents?
- No. Technical experience in software development is the more important foundation for getting started with agent-based systems.
What exactly is an agent-based AI system?
- It consists of AI agents that handle tasks independently and can, for example, integrate tools, data sources, or other agents to do so.
Where are AI agents used in software development?
- They can support development tasks and be used as part of modern applications or automated technical processes.
What is the difference between an AI agent and generative AI?
- Generative AI creates content. AI agents can also perform tasks, use tools, and handle multi-step processes.
Why are multiple AI agents combined?
- Multi-agent systems distribute complex tasks among specialized agents and coordinate their collaboration within a shared process.
What role does security play in autonomous AI agents?
- Clear security measures restrict actions and access. This is especially important when agents are connected to tools or execution environments.
What topics make sense to explore after getting started with Agentic AI?
- Building on this, multi-agent architectures, orchestration, evaluation, memory management, governance, and the secure operation of agent-based systems are relevant.
