Developing Multi-Agent Systems Training Course
Multi-Agent Systems (MAS) represent a state-of-the-art field within artificial intelligence, where numerous AI agents interact through collaboration or competition in ever-changing environments.
This live, instructor-led training—available both online and in-person—is tailored for advanced AI specialists looking to acquire the expertise needed to design, construct, and deploy MAS solutions that tackle intricate, real-world challenges.
Upon completing this training, attendees will be able to:
- Grasp the core principles governing multi-agent system architectures.
- Execute strategies for communication, coordination, and decision-making within MAS.
- Utilize game theory to model agent interactions and manage conflicts.
- Exploit frameworks such as JADE to develop scalable MAS solutions.
- Tackle common MAS challenges, including scalability, trust issues, and emergent behavior.
Course Format
- Engaging lectures and discussions.
- Ample exercises and practical practice.
- Hands-on implementation within a live laboratory setting.
Customization Options
- For bespoke training arrangements, please reach out to us to coordinate.
Course Outline
Introduction to Multi-Agent Systems
- Overview of Multi-Agent Systems (MAS)
- Applications of MAS across real-world domains
- Comparison with single-agent systems
Architectures for Multi-Agent Systems
- Centralized versus decentralized architectures
- Hybrid and layered approaches to MAS
- Tools and frameworks for MAS development (e.g., JADE, SPADE)
Agent Communication and Coordination
- Communication protocols and languages (e.g., FIPA ACL)
- Coordination techniques: planning, negotiation, and synchronization
- Emergent behavior and self-organization in MAS
Game Theory and Decision Making
- Foundations of game theory for MAS
- Cooperative versus competitive strategies
- Resolving conflicts among agents
Learning in Multi-Agent Systems
- Reinforcement learning in MAS
- Collaborative and adversarial learning dynamics
- Transfer learning and knowledge sharing among agents
Challenges and Advanced Topics
- Scalability and performance in large MAS environments
- Trust and security in agent communication
- Ethical considerations and implications of MAS development
Hands-On Activities
- Implementing a basic MAS for resource allocation
- Simulating agent communication and coordination in a dynamic environment
- Deploying a MAS using a framework like JADE
Summary and Next Steps
Requirements
- A strong foundation in artificial intelligence concepts
- Competence in Python programming
- Knowledge of game theory and distributed systems (recommended)
Target Audience
- AI researchers
- AI engineers
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Developing Multi-Agent Systems Training Course - Enquiry
Related Courses
Agentic Development with Gemini 3 and Google Antigravity
21 HoursAdvanced Antigravity: Feedback Loops, Learning & Long-Term Agent Memory
14 HoursAdvanced Mastra Integrations: APIs, Tools, Enterprise Data & External Systems
21 HoursThis instructor-led training in Nigeria focuses on advanced Mastra integrations, covering APIs, tools, and enterprise data systems. Tailored for intermediate engineers, it teaches secure, scalable integration design and includes hands-on implementation using real-world scenarios and best practices.
Interactive AI Agents: AgentCore Memory, Code Interpreter & Browser Tool in Action
14 HoursAccelerating AI Agent Deployment with AgentCore Runtime & Gateway
14 HoursAntigravity for Developers: Building Agent-First Applications
21 HoursGetting Started with Antigravity: An Introduction to Agent-First IDEs
14 HoursAntigravity for Web Automation & Browser-Based Tasks
21 HoursBuilding Fully Managed AI Agents with AgentCore: From Concept to Production
14 HoursAI Agent Development with Mastra
14 HoursThis live, instructor-led session, offered online or on-site, targets mid-level developers and engineering teams aiming to build scalable and observable AI systems using Mastra.
By the end of the program, participants will be capable of:
- Gaining insight into Mastra’s architecture and its connection with LLMs and external APIs.
- Designing and executing AI agents and workflows in TypeScript.
- Utilizing Mastra’s observability and memory utilities to monitor and refine agent behavior.
- Deploying production-ready AI applications by harnessing Mastra’s framework features.
Mastra Debugging, Evaluation & Quality Assurance for AI Agents
21 HoursThis instructor-led session on Nigeria explores Mastra tools for debugging, assessing, and securing AI agent reliability. Attendees will apply structured metrics, build observability workflows, and craft QA strategies to ensure consistent agent performance in complex settings.
Mastra Ops & Production Engineering: Deploying and Scaling AI Agents
21 HoursThis live training in Nigeria guides technical professionals in deploying and scaling Mastra AI agents for production use. It covers environment setup, observability, and performance optimization to ensure reliable, efficient, and cost-effective agent operations.
Mastra Workflow Automation & Multi-Agent Orchestration
21 HoursThis instructor-led training on Nigeria explores the fundamentals of the Mastra framework for advanced multi-agent orchestration. Participants will learn to architect complex workflows, synchronize parallel tasks, and deploy monitoring tools to ensure reliable distributed systems and enterprise integration.
