Secure Agentic AI Systems: Development and Security Fundamentals

Autonomous AI agents are transforming how we automate complex workflows, but they also introduce novel security vulnerabilities. Developers must understand how to build these systems with a security-first mindset to prevent unauthorized actions and data leaks. In this course, you will transition from understanding basic AI interactions to architecting secure, resilient, and scalable agentic systems. You will learn how to design agents that can safely execute tasks, access databases, and interact with external APIs without compromising system integrity. What you'll learn: Understand core concepts of agentic AI architecture, state management, and tool execution; Implement robust security measures to mitigate prompt injection and unauthorized tool usage; Apply secure retrieval-augmented generation patterns and access controls for vector databases; Configure safe boundaries and sandboxed environments for agent execution; Analyze key vulnerabilities in LLM applications to protect your deployments; Design scalable agent workflows using modern orchestration frameworks and secure API integrations. The course starts with foundational definitions of AI agents and security principles before moving into practical code-based implementation patterns. You will progress through structured written lessons and conceptual scenarios designed to build your practical defense skills. This course is designed for software developers, system architects, and AI enthusiasts who are new to agentic security. No prior security experience is required. Start reading today to build AI agents that are both highly capable and thoroughly secured.