ScienceVerseLearning path: AI agents
The concepts to learn before AI agents, in order. Each step links to its page and, where one exists, its quiz.
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Your path
- AI basicsDirect prerequisite
Artificial intelligence (AI) is the field of building machine-based systems that, for a given set of objectives, produce outputs such as predictions, recommendations or decisions. Early AI tried to write intelligence down as hand-made rules; much of modern AI instead learns patterns from data. Today's systems can be remarkably strong at some tasks and surprisingly weak at others, so knowing what an AI system was built and tested to do matters as much as knowing what it can do.
- Language modelsDirect prerequisite
A language model is a system that assigns probabilities to sequences of words — in practice, it repeatedly predicts a probability for each possible next token (a word or piece of a word) given the text so far. Large language models are neural networks with billions of parameters trained on vast amounts of text, and they can carry out many tasks described in plain language. Because they generate statistically likely text rather than looking facts up, they can state false things confidently ('hallucination'), and they reproduce biases in their training data.
- AI agentsYour goal
An AI agent is a system that perceives its environment, decides what to do and takes actions towards a goal, repeating that loop rather than giving a single answer. Today's best-known agents wrap a language model in a loop with memory, planning and tools — search, calculators, code, a computer's mouse and keyboard — so it can carry out multi-step tasks. Agents have improved quickly on benchmarks (as of the AI Index 2026) but still fail often, and acting in the world raises new risks such as compounding errors and prompt injection, so their autonomy needs limits and human oversight.