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.
AI Agents: Multi-Agent Design & Governance
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
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Recently updated!
December 2025
Taught in English
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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