Foundations

Large language models (LLMs) generate text through next-token prediction, using transformer architectures with self-attention mechanisms to process and produce sequences of language. Understanding how scale, training data, and architecture choices affect model capabilities is essential for building effective agents, because these fundamentals explain why models can follow instructions, use tools, and reason through complex problems.

Every agentic tool, from integrated development environment (IDE) agents to custom pipelines, ultimately sends HTTP requests to a model application programming interface (API) and processes the structured response, making direct API fluency the foundation for building anything beyond pre-built tools. You need to understand request anatomy, including authentication, endpoints, and model parameters, as well as response handling for token usage, finish reasons, and tool call outputs, and the differences between providers such as OpenAI's chat completions, Anthropic's Messages API, and Google's Gemini API.

Large language model (LLM) pricing runs on tokens, the chunks of text models process as input and output, with output tokens typically costing 3 to 5 times more than input tokens and costs accumulating fast inside agent loops that call the model repeatedly. An unoptimized agent loop can burn through hundreds of dollars per day in production, so understanding tokenization, context window costs, and price differences between models is a prerequisite for building economically viable systems at scale.