LLM Agents in R: A Hands-On Interactive Lesson

An LLM agent is a plain language model wrapped in a small loop that lets it take real actions. This interactive lesson builds that loop from the ground up, so you can read any agent, reason about it, and build one yourself in R.

Most explanations of agents either stay at the buzzword level or drop you straight into a framework. This lesson starts one level deeper, with what a plain model can and cannot do on its own, and builds up until you can wire a tool, run the loop in R, and add the guard rails that keep it safe.

It is a guided, interactive experience: you step through a real agent trace one move at a time, answer checkpoints as you go, and write R yourself.

What the lesson covers

You start with the gap that makes agents interesting: a plain language model only produces text, so it cannot look up today's number, run a query, or check its own answer. From there the lesson builds the machinery piece by piece, the tool the model can request, the ReAct loop of Thought, Action and Observation that strings tool calls together, and the guard rails (a step budget, call validation, least privilege) that keep the loop safe, then puts it to work in R with the ellmer package.

Start the lesson: LLM Agents in R

Who this is for

You can run R and write a simple function, and you have used a chatbot at least once. You do not need any machine learning background. Everything else, what a token is, what a tool is, how the loop runs, is built here from scratch.

What you will be able to do

• Tell a plain language model apart from an agent that can take actions
• Trace the Thought, Action, Observation loop that lets a model act
• Wire a tool and run the loop in R with ellmer, the framework that hides the loop for you
• Name the three failure modes (hallucinated calls, runaway loops, prompt injection) and the defense for each

Ready? Begin the lesson.