1 min lesson
Two proof points to keep on hand
Name the parts in "Two proof points to keep on hand" and give the practical job of each one.
Step 1 of 2
Two proof points to keep on hand
- Same model, better results
- Independent analysis shows a given model (e.g. Claude) performs better inside the Cursor harnessCursor's hosted layer around each model (context selection, caching, retries) that makes the same model run better and cheaper than calling it directly. Press Enter for the full definition. than called directly, because Cursor builds a model-specific harness per model rather than one generic pipe.
- ~47% fewer agent tokens
- Dynamic context discovery (since v2.4) reads from files, tools and MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. and pulls in only what it deems relevant; combined with caching it cuts agent tokens by roughly 47%, even when running another company's model.
No native memory - the harness simulates it
A misconception worth correcting cold: the model has no native memory. The sense that an agent 'remembers' comes entirely from the harness re-assembling context each turn. So you don't give an agent memory by hoping - you give it memory by referencing other chats or by persisting information as a skill, rule, command or hook. Output quality reduces to two levers: which model (~60-70%) and what context you supply (~30-40%) - and context is the lever you control.