1 min lesson
Ground that claim in what Cursor's harness does
Use "Ground that claim in what Cursor's harness does" to explain each part and the role it plays.
Step 1 of 2
Ground that claim in what Cursor's harness does. Between your prompt and the model provider sits deep proprietary cloud infrastructure, 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., doing optimization, context enrichment, caching and compaction around the model, tuned per frontier model.
- Same model, better results
- On Cursor's account, a given model performs better inside its per-model harness than called directly, because Cursor builds a model-specific harness for each model instead of one generic pipe.
- ~47% fewer agent tokens
- On Cursor's measure, dynamic context discoveryThe agent pulling only the relevant parts of files, tools and MCP servers into context as needed, instead of loading everything up front. Press Enter for the full definition. (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.
The model has no native memory. The sense that an agent 'remembers' comes from the harness re-assembling context each turn. To give an agent memory, reference other chats or persist the information as a skill, rule, command or hook. Output quality comes down mostly to which model you pick and what context you supply, and context is the lever you control.