AI Agents: Multi-Agent Design & Governance

Overview

This course explores the design and governance aspects of multi-agent AI systems - autonomous agents that collaborate, compete, and coordinate to achieve complex goals. Learners will gain a deep understanding of how to design, build, and govern multi-agent ecosystems, from defining core agent capabilities to orchestrating interactions at scale. The course emphasizes real-world applications, exploring how leading companies like LinkedIn, Anthropic, and Amazon deploy agentic AI to solve enterprise problems. Learners will explore the principles of coordination, communication protocols, and governance models, along with ethical and regulatory considerations for safe deployment. This course is ideal for AI enthusiasts, software developers, data scientists, and product managers who want to understand how multi-agent systems work in real-world environments. It’s also valuable for professionals working on AI governance, system design, or scalable automation projects. Learners should have a basic understanding of AI concepts and general computer science principles. No advanced AI or governance experience is required, making this course accessible to anyone eager to explore multi-agent systems and their design. By the end of the course, learners will have a practical foundation to design multi-agent workflows, evaluate performance trade-offs, and implement governance strategies that ensure responsible and efficient agent collaboration in business and research environments.

Syllabus

Foundations of AI Agents and Multi-Agent Systems

This module introduces learners to the fundamental concepts of AI agents, their challenges, and the aspects behind developing multi-agent systems, providing a solid groundwork. Learners will explore how agents perceive, reason, and act within complex environments, as well as the key components that define their architecture.

Designing Robust Multi-Agent AI Systems

In this module, we dive into the dynamics of multi-agent AI systems, exploring how multiple agents coordinate, communicate, and collaborate to achieve shared goals. Students learn about interaction models, communication protocols, and strategies for building scalable, cooperative agent networks. The focus is on understanding why collaboration is critical and how it enhances system intelligence, adaptability, and performance.

Governance, Compliance and Risks in Multi-Agent System

This module focuses on the architectural design of multi-agent systems, including planning, task decomposition, and workflow orchestration. It also examines governance, regulatory considerations, and security best practices necessary for deploying agents safely and ethically. By the end, learners will know how to design robust multi-agent ecosystems that align with real-world constraints and operate within responsible AI frameworks.