AI has become a buzzword in 2025, especially concerning AI agents, as Marina Wyss explains in her comprehensive YouTube course titled “AI Agents in 38 Minutes – Complete Course from Beginner to Pro,” published on December 9, 2025. The course encompasses beginner to advanced levels, distinguishing what AI agents are and their wide array of potential uses, from handling simple tasks like data extraction to complex workflows like customer service.
Marina begins with the fundamentals, defining AI agents as akin to human workers who iteratively refine tasks rather than accomplishing them in one sweep. She explains that AI agents are well-suited for multi-step processes requiring iteration, leveraging examples from legal research to customer support systems. However, the depth and detail come at a cost of complexity, highlighting the importance of discerning when to use AI agents—particularly for high complexity, low precision tasks.
The sponsorship by Kimi and descriptions of their agentic models like Kimi K2 showcase the scalability of AI tools for managing large context tasks affordably. While compared to leading-edge models like GPT-5, Kimi offers a cost-effective alternative with openness being a key feature of its models.
In the intermediate section, Marina delves into system performance evaluation, emphasizing the importance of setting performance metrics either on component levels or end-to-end outputs. She highlights memory and guardrails as integral in improving reliability, ensuring agents process data accurately through feedback loops, thus reducing the possibility of generating inaccurate content.
Moving to the advanced section, the focus shifts to architecting real-world agent systems. Wyss explores task decomposition, drawing on patterns like functional and spatial decomposition to manage workload efficiently. She suggests various design patterns for improving system quality, including reflection, effective tool use, planning, and multi-agent collaboration. These methods add layers of intelligence and autonomy to systems, though they do bring challenges like redundancy and serialization that need vigilance.
A standout segment of the course is the demo of a no-code system, illustrating how AI agents can be practically implemented without deep technical knowledge. Wyss utilizes AI tools for everyday productivity, creating a study assistant in Crew AI that efficiently curates resources and creates study plans.
In conclusion, Wyss manages to balance technical depth with accessibility, making complex AI concepts digestible for viewers ranging from enthusiasts to seasoned engineers. Her coverage of advanced topics is both sophisticated and pragmatic, offering insights into the structural underpinnings of current AI technologies while addressing security concerns inherent in this evolving field.