Cursor vs Cognition: Opposite Takes on Agent Search
Source: https://x.com (tweet) Author: unknown Date: 2025-2026
Summary
Two leading coding agent companies have opposite architectures for code search: Cursor uses custom embeddings trained on agent traces (12.5% accuracy improvement); Cognition rejects embeddings entirely, instead training models to use grep with 8x parallel tool calls. Both work.
Key Claims
- Cursor: Custom embeddings trained on agent traces → +12.5% accuracy
- Cognition: No embeddings; models trained to use grep with 8x parallel tool calls
- Both approaches can succeed — architecture is not settled
- The divergence is philosophically interesting: symbolic (grep) vs. neural (embeddings)
Concepts
- Coding Agents — search strategy as core architectural decision
- Agent Memory — embeddings as a form of indexed agent memory