Context

The relevant information the agent has access to right now. The abstract noun — not the raw input the model sees (that's the context window), not the running history (that's the session), but _what the agent knows that's pertinent to the task_. "Loading something into context" means making it part of this set; "context engineering" is the discipline of curating it.

The three terms separate cleanly:

Term	What it names
Context	The task-relevant information the agent currently has
Context window	The literal token sequence the model sees per request
Session	The running conversation the harness stores

The separation matters because context is a measure of quality, not quantity. A context window can be nearly full and the context still poor — thousands of tokens of stale tool output, none of it about the task at hand. It can also be nearly empty and the context excellent: the one type definition the task turns on.

Most day-to-day failures trace back to context. When the agent invents an API, contradicts a decision, or guesses at a schema, the first question is what was in context when it did — usually the relevant fact was never loaded, or was buried under attention degradation. The fix is curation: load what the task needs, keep out what it doesn't.