Developing Multi-Agent Systems Training Course
Multi-Agent Systems (MAS) represent a forefront domain within artificial intelligence, characterized by multiple AI agents interacting—either cooperatively or competitively—within dynamic settings.
This instructor-led, live training (available online or onsite) targets advanced AI professionals aiming to master the skills necessary to design, construct, and deploy MAS capable of addressing complex, real-world challenges.
Upon completion of this training, participants will be equipped to:
- Grasp the fundamental principles governing multi-agent system architectures.
- Execute strategies for effective communication, coordination, and decision-making within MAS.
- Utilize game theory to model agent interactions and manage conflicts.
- Employ frameworks such as JADE to develop scalable MAS solutions.
- Tackle key challenges like scalability, trust, and emergent behavior in MAS.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- To arrange a tailored training session for this course, please contact us directly.
Course Outline
Introduction to Multi-Agent Systems
- Overview of Multi-Agent Systems (MAS)
- Applications of MAS in real-world domains
- Comparison with single-agent systems
Architectures for Multi-Agent Systems
- Centralized vs 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
- Basics of game theory for MAS
- Cooperative vs 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
- Strong grasp of artificial intelligence concepts
- Proficiency in Python programming
- Familiarity with game theory and distributed systems (recommended)
Target Audience
- AI researchers
- AI engineers
Open Training Courses require 5+ participants.
Developing Multi-Agent Systems Training Course - Booking
Developing Multi-Agent Systems Training Course - Enquiry
Developing Multi-Agent Systems - Consultancy Enquiry
Upcoming Courses
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 Serbia delves into advanced Mastra integrations, encompassing APIs, tools, and enterprise data systems. Tailored for intermediate engineers, the course emphasizes secure and scalable integration design through hands-on implementation, real-world scenarios, and industry 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 training session (available online or onsite) is tailored for mid-level software engineers and technical teams seeking to construct scalable, observable AI systems utilizing Mastra.
Upon completion of this course, participants will be equipped to:
- Grasp Mastra's architectural design and its methods for integrating with LLMs and external APIs.
- Architect and develop AI agents and workflows using TypeScript.
- Leverage Mastra's observability and memory capabilities to oversee and refine agent performance.
- Launch production-grade AI applications by utilizing Mastra's framework features.
Mastra Debugging, Evaluation & Quality Assurance for AI Agents
21 HoursThis instructor-led training on Serbia explores Mastra tools for debugging, evaluating, and ensuring AI agent reliability. Participants will apply structured metrics, implement observability workflows, and design QA strategies to maintain consistent agent performance in complex environments.
Mastra Ops & Production Engineering: Deploying and Scaling AI Agents
21 HoursThis live training in Serbia walks technical professionals through the process of deploying and scaling Mastra AI agents for production. It addresses environment setup, observability, and performance tuning to guarantee reliable, efficient, and cost-effective agent operations.
Mastra Workflow Automation & Multi-Agent Orchestration
21 HoursThis instructor-led training in Serbia focuses on the fundamentals of the Mastra framework for sophisticated multi-agent coordination. Participants will learn to architect complex workflows, manage parallel tasks, and deploy monitoring tools to ensure reliable distributed systems and seamless enterprise integration.
