AI Agents: Multi-Agent Design & Governance

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

• Define core concepts and capabilities of AI agents and multi-agent systems
• Design effective multi-agent AI systems for various tasks and implement communication protocols and workflows
• Apply governance models and regulatory frameworks to ensure safe and compliant AI agent operations

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work