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Multi-Agent AI Systems Architecture & Development Training Course

Teaches how to architect systems in which specialized AI agents collaborate, communicate, share tasks and coordinate actions to solve complex business problems.

AIGA · AI Agents & Autonomous SystemsAll Levels#2111ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Course Overview

This advanced training course provides a comprehensive framework for understanding, designing, developing, and deploying Multi-Agent AI Systems. It focuses on building intelligent systems in which multiple specialized AI agents collaborate, communicate, distribute tasks, and coordinate actions to solve complex business and operational problems. The program examines the architecture of multi-agent systems, including agent roles, communication mechanisms, task allocation, planning, memory and context management, tool utilization, decision-making, state management, and workflow orchestration. Participants will explore how multiple agents can collaborate, exchange information, and execute interdependent tasks within a coordinated environment. Particular attention is given to multi-agent architecture patterns, communication protocols, dependency and conflict management, workflow coordination, integration with AI models, APIs, databases, enterprise applications, and external tools. The course also addresses scalability, reliability, security, governance, observability, and performance management for multi-agent environments. Through hands-on exercises, architecture workshops, case studies, and an integrated development project, participants will develop the ability to identify suitable multi-agent use cases, design multi-agent architectures, develop communication and coordination mechanisms, integrate agents with external systems, evaluate system performance, and prepare production-ready multi-agent AI solutions.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Understand the fundamental principles and architecture of Multi-Agent AI Systems.
  • Identify suitable use cases for multi-agent solutions.
  • Design agent roles and responsibilities within a multi-agent environment.
  • Apply task allocation and coordination strategies across multiple agents.
  • Design communication and information-sharing mechanisms between agents.
  • Manage context, memory, and state across multi-agent environments.
  • Integrate agents with tools, APIs, databases, and enterprise systems.
  • Design multi-agent workflows for complex business and operational tasks.
  • Manage conflicts, dependencies, failures, and recovery processes between agents.
  • Evaluate the performance, reliability, scalability, and efficiency of multi-agent systems.
  • Apply security, governance, monitoring, and observability requirements.
  • Develop strategies for deploying and scaling Multi-Agent AI Systems.

Who Should Attend

This course is designed for AI Engineers, Machine Learning Engineers, Software Engineers, Data Scientists, AI Solution Architects, Automation Engineers, Systems Engineers, and technology professionals involved in developing intelligent applications and advanced AI solutions. It is also suitable for Solution Architects, Technical Architects, Digital Transformation Professionals, IT Managers, Innovation Leaders, Technical Product Managers, Project Managers, and professionals responsible for developing AI-driven automation and intelligent enterprise systems. The program is particularly relevant to organizations seeking to develop intelligent solutions capable of distributing complex tasks among specialized agents, integrating with enterprise systems, automating multi-step workflows, and improving the efficiency and responsiveness of business and operational processes.

Learning Outcomes

  • Upon completion of the course, participants will be able to:
  • Assess Multi-Agent AI System architectures against specific business requirements.
  • Determine when a multi-agent approach is more appropriate than a single-agent solution.
  • Design agent roles and define individual agent responsibilities.
  • Develop strategies for task allocation and coordinated execution.
  • Design communication protocols and information-sharing mechanisms between agents.
  • Implement shared context, memory, and state-management mechanisms.
  • Integrate agents with tools, APIs, databases, knowledge sources, and enterprise applications.
  • Develop multi-agent workflows for complex operational processes.
  • Manage conflicts, dependencies, failures, and recovery mechanisms.
  • Evaluate system accuracy, reliability, performance, and resource utilization.
  • Implement security, governance, monitoring, and observability controls.
  • Prepare a practical roadmap for developing, deploying, and scaling Multi-Agent AI Systems.

Course Outline

Course Outline

Day 1

Multi-Agent AI Fundamentals & System Architecture

  • Evolution of AI systems toward multi-agent architectures
  • Single-agent versus multi-agent systems
  • Core components of multi-agent architectures
  • Agent roles and responsibility allocation
  • Agent goals and task boundaries
  • Complex task planning and decomposition
  • State and context management
  • Shared memory across agents
  • Enterprise use cases for multi-agent systems
  • Practical Application: Designing an initial Multi-Agent AI architecture for an enterprise business process
Day 2

Agent Design, Communication & Task Coordination

  • Multi-agent system design patterns
  • Designing specialized agent roles
  • Task allocation strategies
  • Multi-stage planning and execution
  • Agent communication protocols
  • Information and message exchange
  • Shared context management
  • Decision and action coordination
  • Dependency management between agents
  • Practical Workshop: Building a coordinated multi-agent model for executing a multi-stage business task
Day 3

Multi-Agent Development & Tool Integration

  • Integrating AI models with agents
  • Tool and function calling
  • API integration
  • Database and knowledge-source integration
  • Retrieval-Augmented Generation in multi-agent systems
  • Enterprise system integration
  • Workflow automation
  • Execution state and result management
  • Error handling and recovery mechanisms
  • Practical Application: Developing a multi-agent system integrated with external tools and data sources
Day 4

Multi-Agent Evaluation, Security & Governance

  • Agent performance evaluation methodologies
  • Measuring task completion and output accuracy
  • Testing collaboration and coordination between agents
  • Failure and conflict analysis
  • Managing incorrect actions and unreliable outputs
  • Human oversight and controlled autonomy
  • Security and access control
  • Data privacy and governance
  • Monitoring, logging, observability, and auditability
  • Practical Workshop: Developing an evaluation, security, and governance framework for a Multi-Agent AI System
Day 5

Deployment, Scaling & Multi-Agent Strategy

  • Production architecture for Multi-Agent AI Systems
  • Agent deployment and lifecycle management
  • Scalability and performance management
  • Cost and resource optimization
  • System monitoring and continuous improvement
  • Integration with enterprise processes and systems
  • Governance and autonomy boundaries
  • Change management and technical capability development
  • Scaling multi-agent solutions across business functions
  • Final Integrated Workshop: Developing a comprehensive Multi-Agent AI Systems Development & Deployment Roadmap covering system architecture, agent roles, task allocation, communication, memory, tool integration, knowledge retrieval, security, governance, monitoring, deployment, scalability, and continuous improvement.

Upcoming Dates

Selected date

Online · 27 – 31 Dec 2026

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CityDatesPrice per ParticipantEarly Bird
OnlineScheduled
27 – 31 Dec 2026€2,700€2,430until 7 Dec
DubaiScheduled
27 – 31 Dec 2026€4,700€4,230until 7 Dec
AmmanScheduled
27 – 31 Dec 2026€4,000€3,600until 7 Dec
GenevaScheduled
28 Dec 2026 – 1 Jan 2027€7,300€6,570until 7 Dec
MunichScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
IstanbulScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
DohaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
Kuala LumpurScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
AmsterdamScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
Sharm El-SheikhScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
ManamaScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
ParisScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
MilanScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
RomeScheduled
28 Dec 2026 – 1 Jan 2027€7,000€6,300until 7 Dec
LisbonScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
MadridScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
MadinahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
BangkokScheduled
28 Dec 2026 – 1 Jan 2027€6,700€6,030until 7 Dec
MakkahScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
FrankfurtScheduled
28 Dec 2026 – 1 Jan 2027€6,200€5,580until 7 Dec
CairoScheduled
28 Dec 2026 – 1 Jan 2027€3,800€3,420until 7 Dec
MuscatScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
ViennaScheduled
28 Dec 2026 – 1 Jan 2027€6,400€5,760until 7 Dec
BarcelonaScheduled
28 Dec 2026 – 1 Jan 2027€6,000€5,400until 7 Dec
TunisiaScheduled
28 Dec 2026 – 1 Jan 2027€4,300€3,870until 7 Dec
RabatScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
RiyadhScheduled
28 Dec 2026 – 1 Jan 2027€4,800€4,320until 7 Dec
LondonScheduled
28 Dec 2026 – 1 Jan 2027€5,800€5,220until 7 Dec
CasablancaScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec
MarrakeshScheduled
28 Dec 2026 – 1 Jan 2027€4,700€4,230until 7 Dec

Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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