Multi-Agent Systems Training – Basic Training in the Use of Multi-AI Agents and Agent-Based Workflows
Upon request
2 days
Munich, Online, or In-House
Upon request
German or English
Digital Certificate of Participation
Modern AI applications are increasingly evolving from individual chatbots into intelligent multi-agent systems. In these systems, multiple specialized AI agents work together on complex tasks, coordinate information, access tools, and automate entire workflows. Companies are increasingly deploying such systems to streamline processes, make better use of knowledge, and productively integrate AI into existing structures. Our Multi-Agent Systems training course covers the fundamentals of modern agent systems and demonstrates how multi-agent architectures with “human-in-the-loop” control points can be built, orchestrated, and deployed productively and securely. This is complemented by coverage of the management and governance of agent systems—from role-based and approval concepts to compliance requirements, monitoring, and auditability. The training combines an understanding of architecture and workflows with hands-on exercises involving AI agents, automation, and real-world process examples.
Content and Structure of the Multi-Agent Systems Training Course
The introductory course covers technical fundamentals, an understanding of system architecture, and practical concepts for modern multi-agent systems.
- Introduction to Agent Systems, LLM-Based Workflows, and Modern AI Architectures
- Terms and Concepts: Agent, Tool, Memory, Harness, and Context
- Differences Between Single-Agent and Multi-Agent Approaches
- Multi-Agent Topologies
- Supervisor
- Hierarchy
- Swarm
- Debate
- Planner-Executor
- Communication and Collaboration Patterns Among Agents
- Architectural Principles for Scalable and Robust Agent Systems
- The Roles of Product Owners, Architecture Teams, and Development Teams in the Agentic AI Environment
- Development of specialized agent roles such as Planner, Executor, Researcher, or Critic
- Methods for Task Decomposition and Goal Structuring
- Definition of Prompt Contracts, Interfaces, and Acceptance Criteria
- Building Robust Agent Workflows with Delegation and State Logic
- Fault Tolerance Through Guardrails, Retry Mechanisms, and Checkpoints
- Introduction to Agent-Based Decision Logic and Workflow Orchestration
- Exercises for Developing Multi-Stage Agent Processes
- Function Calling and API Integration in Agent-Based Systems
- Use of RAG Architectures for Knowledge-Based Agents
- Integration of databases, CRM systems, ticketing systems, and file sources
- Shared Memory vs. Private Memory and Context Management
- Authorization and Security Concepts (“Least Privilege”)
- Building Productive AI Workflows with External Tools and Services
- Integration Patterns for Existing Enterprise Architectures
- Evaluation of Agent Systems Using Golden Sets, Rubrics, and Regression Tests
- Tracing and Traceability of Prompt and Tool Calls
- Dealing with Hallucinations, Risk Assessments, and Quality Control
- Cost, Token, and Latency Optimization in Production Environments
- Monitoring, Logging, Incident Handling, and Rollback Strategies
- Deployment Models: Cloud, On-Premises, and Hybrid Architectures
- Operational Reliability, Scalability, and Lifecycle Management of Agent Systems
- Protection against prompt injection, data leakage, and misuse
- PII Handling, Data Protection, and Compliance Requirements
- Governance Structures for Agentic AI and the Responsible Use of AI
- Security Patterns for Agent-Based Systems and Tool Access
- Roles, Responsibilities, and Approval Processes in Production Operations
- Best Practices for Scalable Enterprise Agent Systems
- Outlook on Autonomous Agent Systems and Future Developments
Concrete Results – What You Can Put into Practice Immediately After the Multi-Agent Systems Training
After completing the training, you will understand modern multi-agent architectures and be able to design your first functional agent systems.
- Building Multi-Agent Systems and AI Agents in a Structured Way
- Defining Role Models and Agent Workflows
- Orchestrating and Automating Agent-Based Processes
- Connect AI agents to data sources and tools
- Take Governance and Security Considerations into Account During Operations
- Improving the Quality, Monitoring, and Stability of Agent Systems
- Integrating Multi-Agent Approaches Productively into Business Processes
Who is the Multi-Agent Systems Training course intended for?
This training is intended for:
- Product Owners and Project Managers
- Architecture and Development Teams
- AI and Automation Teams
- Companies that want to put AI agents to productive use
- Specialists and Executives in the Field of Digital Transformation
A basic interest in AI systems and workflows is helpful.
Eligibility Requirements for the AI Sales Training
A basic technical understanding of digital systems or AI applications is a plus, but not a mandatory requirement.
Custom Multi-Agent Workshops & Training Sessions
We design customized in-house training programs—tailored perfectly to your needs.
- Customized Training Programs for Companies and Teams
- Integration of Custom Workflows, Tools, and Systems
- A combination of an architecture workshop and hands-on sessions
- Focus on Productive Automation and AI Workflows
About Us as a Training Provider
We are a management consulting firm with expertise in AI transformation, workflow automation, and modern system architectures. Our multi-agent training programs combine technological fundamentals with productive business applications and help teams implement agent-based AI systems in a secure and sustainable manner.
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Expertise in Multi-Agent Systems – Your Instructors
Jonas helps companies implement modern AI architectures and agent-based workflows. His focus is on productive multi-agent systems, automation, and the integration of modern AI technologies into business processes.
Thorsten combines experience in transformation, collaboration, and modern organizational development with practical AI applications. He helps companies integrate agent-based systems into teams, processes, and digital strategies in a sustainable way.
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- In-Depth: Understanding the Architectures, Role Models, and Orchestration of Modern AI Agents
- Proven in Practice: Designing Multi-Stage Agent Workflows Based on Specific Business Processes
- Integrated: Connect AI agents to data sources, APIs, and existing tools
- For Sure: Consider "Human in the Loop," Governance, Data Protection, and Security from the Very Beginning
- Operational Readiness: Systematically Managing Quality, Monitoring, Costs, and Scalability
TISAX and ISO certification apply only to the Munich location
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TISAX and ISO certification apply only to the Munich location
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FAQ – Frequently Asked Questions About the Multi-Agent Systems Training Course
Multi-agent systems consist of several specialized AI agents that work together to perform tasks and exchange information. Unlike individual chatbots, they can structure, delegate, and automate complex processes. This results in more powerful and flexible AI workflows.
No. The training course explains the fundamentals in an easy-to-understand way and focuses on architecture, processes, and practical concepts. Prior technical knowledge is helpful but not required.
For example, multi-agents can support knowledge work, research, analysis, customer service, or internal workflows. They are particularly useful for complex processes involving many dependencies and data sources.
Yes. The seminar provides an overview of relevant AI tools, agent frameworks, and integration options. The focus is on productive enterprise applications and modern workflow architectures.
This is very important. Autonomous or semi-autonomous agent systems, in particular, require clear security, governance, and compliance mechanisms. That is why the training also covers data protection, access policies, and secure system architectures.
Yes. Especially with in-house training, existing workflows, data sources, or automation goals can be integrated. This results in concrete solutions tailored to your organization.