Why AI Agent Builders Need a Curated Learning Path

In a recent LinkedIn post, Luís Rodrigues discusses a collection of more than 50 resources for people learning about AI agents. This article reports on Rodrigues’s post and its recommendations; it is not written by Rodrigues. He presents the collection as a practical starting point for readers navigating a broad and fast-moving field.

“50+ hand-picked resources (in one Google Doc).”

A broad collection, organized for exploration

Rodrigues groups the resources into categories including videos, repositories, guides, books, research papers and courses. The examples named in the post range from introductory material on large language models to Anthropic’s guide to building effective agents, Google’s agent publications, the ReAct paper and courses on agents and vector databases.

That structure gives readers several ways into the subject. Someone seeking a conceptual overview might begin with a guide or video, while a more technically focused learner could turn to a repository, paper or course. Rodrigues does not rank the resources or claim that they form a formal curriculum; the post offers a curated directory for readers to browse.

Small steps over an overwhelming reading list

Rodrigues’s closing advice emphasizes choosing a manageable starting point rather than trying to work through every link at once:

“Lots of resources; pick one or two and let’s go!”

That suggestion gives the post a practical focus. A list of dozens of links can be difficult to act on, but selecting one or two resources turns discovery into a more concrete next step. In Rodrigues’s framing, the collection is useful as a menu, not a requirement to consume everything.

From study material to emerging workflows

Rodrigues also connects the resource list to his newsletter, Build What Matters, which he says covers AI agents and emerging workflows. The post therefore places the links within a larger learning agenda: readers can use the collection to explore foundational material while following Rodrigues’s ongoing commentary on the area.

According to Rodrigues, the full document is available through a link in the post. The listed examples indicate the range of material included, but the post itself does not describe how each resource was selected or assessed. Readers can use the categories to identify material relevant to their interests and evaluate it for themselves.

A starting point, rather than a complete course

Rodrigues’s post offers a straightforward proposition: gather useful AI agent learning resources in one place, then encourage readers to begin with a small selection. Its value lies in that combination of breadth and a simple call to action. For people trying to make sense of agent development, the collection may help narrow the search for a first guide, course or paper.