GenAI Foundations and AI Agents Development

Ready to move beyond reactive AI systems to autonomous agents that think, plan, and execute complex tasks independently? Most AI implementations remain limited to simple question-and-answer interactions, missing the transformative potential of truly autonomous AI workers that can reason, collaborate, and solve problems without constant human guidance. This advanced course transforms you into an autonomous AI architect who builds intelligent agents that operate like digital team members. You'll master the complete agent development lifecycle using cutting-edge frameworks like CrewAI, implement sophisticated tool integration that enables agents to interact with real-world systems, and design multi-agent orchestration where specialized agents collaborate to solve complex problems. Through intensive hands-on development, you'll create customer support agents with advanced reasoning capabilities, implement agent safety frameworks for production deployment, and build coordination systems that manage multiple autonomous agents working together. This course is designed for AI/ML engineers building autonomous systems, software architects crafting agent-based frameworks, and product engineers seeking to implement intelligent automation. It also serves technical leaders exploring the potential of agentic AI to create scalable, context-aware solutions. Whether you're working on enterprise-grade agent systems or pioneering new intelligent workflows, this course provides a practical and robust foundation. Participants should have a solid foundation in generative AI concepts, prompt engineering, and retrieval-augmented generation (RAG) techniques. A strong command of Python programming is essential, along with familiarity with common AI/ML concepts and working with APIs. Learners should also possess a firm understanding of object-oriented programming principles and distributed systems to effectively engage with the course’s advanced technical content. By the end of this course, learners will be able to construct autonomous AI agents using the CrewAI framework with integrated tools and decision-making logic. They will implement advanced multi-agent systems with coordination protocols and delegated task handling, deploy customer support agents that integrate with knowledge bases and manage escalations, and apply agent safety strategies and testing protocols to ensure robust, production-ready deployment. Additionally, learners will gain hands-on experience through real-world projects that reinforce architectural design, coordination flows, and evaluation of agent behavior in complex environments.