Context

Context is everything the model sees on a given turn: the full set of tokens loaded into its context window before it produces its next output. That typically includes the system prompt (role and instructions), the conversation history so far, any tool call results from prior turns, attached files or code snippets, retrieved documents, and explicit memory the harness has surfaced.

Context is the only place a model can look for information — it has no hidden state and no memory between API calls. Whatever is in context on this turn is what the model "knows" right now. Everything else, from yesterday's conversation to the contents of a file on disk, must be reloaded into context for the model to use it.

Because context windows are finite — typically 128K–2M tokens today, depending on the model — and every token costs money and attention, context management is one of the hardest problems in agentic systems. Harnesses spend significant effort deciding what to include, what to summarize, when to compact older turns, and how to surface the right files, tool results, and memories without drowning the model in irrelevant text.

Good context engineering — clear system prompts, tight tool results, selective history, and retrieval that surfaces only what's needed — often matters more than raw model capability. A frontier model with bad context will underperform a weaker model given the right information.

See also: context window, system prompt, RAG.