• phone icon +44 7459 302492 email message icon support@uplatz.com
  • Register

BUY THIS COURSE (GBP 12 GBP 29)
4.8 (2 reviews)
( 10 Students )
 

Multi-Agent AI Systems

Learn how to design, coordinate, and deploy multi-agent AI systems using LLM-based agents, planning, communication protocols, and real-world orchestra
( add to cart )
ALL COURSES Course URL
Save 59% Offer ends on 31-Dec-2026
Course Duration: 10 Hours
  Price Match Guarantee   Full Lifetime Access     Access on any Device   Technical Support    Secure Checkout   Course Completion Certificate
New & Hot
Highly Rated
Popular
Coming soon (2026)

Students also bought -

  • PEFT
  • 10 Hours
  • GBP 29
  • 10 Learners
Completed the course? Request here for Certificate. ALL COURSES

As artificial intelligence systems grow more capable, a single model or agent is often no longer sufficient to solve complex, real-world problems. Modern AI applications increasingly require multiple intelligent agents that can reason independently, collaborate with each other, delegate tasks, share memory, and adapt dynamically to changing environments. This paradigm — known as Multi-Agent AI Systems — is rapidly becoming one of the most important architectural patterns in advanced AI engineering.
 
Multi-agent systems (MAS) are inspired by real-world organizations, where teams of specialists work together toward shared objectives. In AI, each agent may have its own role, tools, memory, goals, and reasoning strategy. When properly coordinated, these agents can solve problems that are too complex, ambiguous, or dynamic for a single model. From autonomous software engineering teams and AI research assistants to supply-chain optimization and simulation-based decision-making, multi-agent AI systems are transforming how intelligent applications are built.
 
The Multi-Agent AI Systems course by Uplatz provides a comprehensive and practical introduction to designing, implementing, and deploying collaborative AI agents. This course focuses heavily on LLM-powered agents, where large language models act as reasoning cores for agents that can plan, communicate, execute tools, and coordinate actions. Learners will understand not only how individual agents work, but how systems of agents interact, resolve conflicts, manage shared goals, and operate safely at scale.
🔍 What Are Multi-Agent AI Systems?
 
A multi-agent AI system consists of multiple autonomous agents operating within a shared environment. Each agent can:

  • Perceive information

  • Make decisions independently

  • Communicate with other agents

  • Execute actions or tools

  • Adapt behavior based on feedback

In modern AI, agents are often powered by LLMs and enhanced with tools such as search engines, databases, APIs, code execution, and memory stores. Agents may be:

  • Cooperative (working toward a shared goal)

  • Competitive (optimizing individual rewards)

  • Hierarchical (manager–worker structures)

  • Decentralized (peer-to-peer collaboration)

This course focuses primarily on cooperative and hierarchical multi-agent systems, which are most relevant for enterprise AI, automation, and intelligent workflows.
⚙️ How Multi-Agent AI Systems Work
 
Designing multi-agent systems requires careful coordination across multiple dimensions:
 
1. Agent Architecture
 
Each agent typically includes:

  • A reasoning core (LLM)

  • Short-term and long-term memory

  • Tool access (APIs, code execution, databases)

  • A role definition or persona

  • Goal or task specification

2. Communication & Coordination
 
Agents interact using:

  • Natural language messages

  • Structured protocols

  • Shared memory spaces

  • Event-driven signaling

The course explores message passing, shared blackboards, and mediated coordination.
 
3. Planning & Task Decomposition
 
Multi-agent systems often rely on:

  • Goal decomposition

  • Task allocation

  • Role-based planning

  • Chain-of-thought and plan-and-execute patterns

4. Orchestration & Control
 
Systems may include:

  • Central orchestrators

  • Supervisor agents

  • Voting and consensus mechanisms

  • Conflict resolution strategies

5. Safety & Governance
 
Key concerns include:

  • Hallucination containment

  • Agent alignment

  • Permission boundaries

  • Monitoring and auditability

🏭 Where Multi-Agent AI Is Used in Industry
 
Multi-agent systems are already being applied across industries:
 
1. Software Engineering & DevOps
 
AI agents collaborate as developers, testers, reviewers, and deployers.
 
2. Research & Knowledge Work
 
Research agents search, summarize, critique, and synthesize information collaboratively.
 
3. Business Process Automation
 
Agents handle procurement, finance, HR, and operations workflows.
 
4. Customer Support
 
Multiple agents handle intent detection, retrieval, response generation, and escalation.
 
5. Robotics & Simulation
 
Agents coordinate movement, negotiation, and strategy in shared environments.
 
6. Finance & Trading
 
Agents analyze markets, manage portfolios, and assess risk collaboratively.
 
7. Smart Cities & Infrastructure
 
Traffic control, energy optimization, and emergency response coordination.
🌟 Benefits of Learning Multi-Agent AI Systems
 
By mastering multi-agent AI, learners gain:

  • Advanced AI system design skills

  • Ability to build scalable, modular AI applications

  • Expertise in agent coordination and planning

  • Practical experience with LLM-powered agents

  • Strong foundation for autonomous AI development

  • High-demand skills for next-generation AI roles

Multi-agent architectures are becoming a core competency for advanced AI engineers.
📘 What You’ll Learn in This Course
 
You will explore:

  • Foundations of multi-agent systems

  • Agent roles, memory, and tools

  • LLM-based agent design

  • Communication and coordination patterns

  • Task decomposition and planning

  • Agent orchestration frameworks

  • Safety, monitoring, and governance

  • Real-world multi-agent applications

  • Capstone: build a complete multi-agent AI system

🧠 How to Use This Course Effectively

  • Start with single-agent fundamentals

  • Progress to simple multi-agent collaboration

  • Experiment with role-based agents

  • Build hierarchical and decentralized systems

  • Add safety constraints and monitoring

  • Complete the capstone project step by step

👩‍💻 Who Should Take This Course

  • AI Engineers

  • LLM Developers

  • Machine Learning Engineers

  • Software Architects

  • Automation Engineers

  • Data Scientists

  • Researchers and advanced AI students

Basic Python and familiarity with LLM concepts are recommended.
🚀 Final Takeaway
 
Multi-Agent AI Systems represent the next evolution of artificial intelligence — moving from single-model intelligence to collaborative, autonomous AI ecosystems. By mastering multi-agent design, coordination, and deployment, you gain the ability to build intelligent systems that reason, plan, and act together at scale.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand multi-agent system principles

  • Design autonomous AI agents with roles and goals

  • Implement agent communication and coordination

  • Build hierarchical and cooperative agent systems

  • Integrate tools, memory, and planning

  • Deploy multi-agent AI applications safely

  • Evaluate and monitor agent performance

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Multi-Agent AI

  • History and motivation

  • Single-agent vs multi-agent systems

Module 2: Agent Design

  • Roles, memory, tools

  • LLM-based reasoning

Module 3: Communication & Coordination

  • Messaging patterns

  • Shared memory

Module 4: Planning & Task Decomposition

  • Goal hierarchies

  • Plan-and-execute

Module 5: Orchestration Patterns

  • Supervisor agents

  • Manager–worker models

Module 6: Safety & Governance

  • Permissions

  • Alignment and control

Module 7: Frameworks & Tooling

  • LangChain agents

  • AutoGen

  • CrewAI

  • Semantic Kernel

Module 8: Real-World Applications

  • Business automation

  • Research assistants

Module 9: Deployment & Monitoring

  • APIs and services

  • Logging and observability

Module 10: Capstone Project

  • Build a complete multi-agent AI system

Certification Back to Top

Learners receive a Uplatz Certificate in Multi-Agent AI Systems, validating their skills in collaborative AI architecture, agent orchestration, and intelligent system design.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • AI Engineer

  • LLM Engineer

  • Autonomous Systems Engineer

  • AI Architect

  • Applied AI Researcher

  • Automation Engineer

  • GenAI Product Developer

Interview Questions Back to Top

1. What is a multi-agent AI system?

A system composed of multiple autonomous AI agents that interact and collaborate.

2. How do agents communicate?

Through messages, shared memory, or coordination protocols.

3. What role do LLMs play in agents?

They act as the reasoning and decision-making core.

4. What is a supervisor agent?

An agent that coordinates or manages other agents.

5. Why use multi-agent systems?

To solve complex problems that require collaboration and specialization.

6. What are common risks?

Hallucinations, misalignment, coordination failures.

7. How are tasks distributed?

Through planning, role assignment, or dynamic delegation.

8. What frameworks support multi-agent AI?

LangChain, AutoGen, CrewAI, Semantic Kernel.

9. Are multi-agent systems deterministic?

No, they are often probabilistic and adaptive.

10. Where are multi-agent systems used?

Automation, research, robotics, finance, and enterprise AI.

Course Quiz Back to Top
Start Quiz
Back to Top



BUY THIS COURSE (GBP 12 GBP 29)