Building AI Agent from Scratch with Ruby

Author(s): Alex Chaplinsky

Originally published on Towards AI.

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This article isn’t just about writing code; it’s about the architecture and patterns that underpin AI agent development. Ruby, with its simplicity and readability, serves as an ideal medium for illustrating these concepts. The language’s elegance allows readers to focus on the patterns and principles rather than getting bogged down in complex syntax. The goal is to make the ideas accessible and easy to follow, regardless of your programming background.

Imagine a system that doesn’t just process information but actively interacts with its surroundings. An AI agent perceives its environment, reasons through challenges makes decisions, and takes purposeful actions to achieve specific goals. While this sounds straightforward, the concept of an “agentic AI system” is far from universally defined. Some definitions emphasize autonomy and decision-making, while others focus on collaboration, adaptability, or specific technical implementations.

The term covers a fascinating spectrum of approaches, architectures, and agent types — each tailored to different needs and objectives. For example, rule-based systems rely on predefined logic, while machine learning-based agents adapt their behavior through training on data. Various architectures, such as those described in research papers, range from single-agent frameworks to… Read the full blog for free on Medium.

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Published via Towards AI