Complete Agentic Red-Teaming Bootcamp

About this course

Agentic Red-Teaming is a practical online course designed to help learners understand, test, and improve the security, reliability, and safety of AI agents and autonomous AI systems. As Agentic AI becomes increasingly capable of making decisions, using external tools, accessing data, and executing multi-step tasks, identifying potential vulnerabilities and security risks is essential. This course introduces the principles, techniques, and best practices of red-teaming for AI agents, helping learners discover weaknesses before they can be exploited in real-world applications.

Throughout the course, You will explore how AI agents operate, how they interact with large language models (LLMs), and how agentic workflows can introduce new security challenges. Key topics include prompt injection, jailbreak attempts, tool misuse, insecure data handling, unauthorized actions, excessive agent permissions, and vulnerabilities in multi-agent systems. Learners will understand how to design controlled adversarial tests to evaluate agent behavior, identify failure points, and assess the effectiveness of existing security controls.

By the end of this course, learners will have a solid foundation in Agentic AI security testing and red-teaming methodologies. They will be better prepared to identify potential threats, evaluate the behavior of autonomous AI systems, and apply defensive techniques to build safer and more trustworthy AI agents. This course is suitable for AI engineers, machine learning engineers, cybersecurity professionals, software developers, AI researchers, and technology enthusiasts who want to strengthen their knowledge of AI security and responsible agent development.

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