retoor
af080fe68e
- Approval: re-prompt unknown answers (max 3), shell-only affirmative guidance recovery; destructive actions stay strict - GoalVerifier subagent with strict JSON verdicts, bounded follow-up rounds, nonzero exit plus open turn on unverified one-shots - Retrieval eval (Hit@K/MRR/nDCG/Context Precision), FTS cascade with title weighting, query/read event logging, --eval-retrieval CLI - Supervisor caps (8 parallel, 64 records) and scheduler hardening (4 fires/tick, per-schedule lease, stated misfire policy) - Sandbox box resource trio: 2g/2.0/256 at create, best-effort update for existing boxes, rm -f recovery pointer - Research archive: 12 subjects, 667 sources, 27 conclusions; suites 979 + 16 green
2.5 KiB
2.5 KiB
Subagents
Metadata
| Field | Value |
|---|---|
| Institute | Molodetz Institute — Research & Retrieval Division |
| Subject | prompt-context-scaling |
| Document type | Source Dossier |
| Tier | 1 |
| Source title | Subagents |
| Source URL | https://cursor.com/docs/subagents |
| Source publisher / author | Cursor |
| Source type | vendor docs |
| Source date (published) | Unknown — not stated (living documentation page) |
| Date researched | 2026-10-18 |
| Researched by | tai (agent), model: Muse Spark, on behalf of retoor |
| Research method | both |
| Confidence | High |
| Status | Active |
Summary
Cursor's subagent docs describe context isolation as the primary motive for delegation: each subagent gets a clean window, noisy intermediate output never touches the main conversation, and purpose-built Explore/Browser subagents run on faster, cheaper models. The built-in subagents were designed from analysis of conversations where context limits were actually hit.
Key findings
- "Each subagent operates in its own context window" and "Long research or exploration tasks don't consume space in your main conversation."
- "Intermediate output stays in the subagent. The parent only sees the final summary."
- "The explore subagent uses a faster model by default. This enables running 10 parallel searches in the time a single main-agent search would take."
- "Faster models cost less. Isolating token-heavy work in subagents with appropriate model choices reduces overall cost."
- "These subagents were designed based on analysis of agent conversations where context window limits were hit."
Pros / strengths
- Production evidence from the most-used coding agent that delegation-for-isolation plus cheaper-models-for-bulk-work is the economical shape.
- The foreground/background mode split matches tai's fork/poll split (collect now versus later).
Cons / limitations / caveats
- Product docs for a managed agent; no failure rates or quality comparisons between main-model and fast-model subagent output.
- Cursor-specific built-ins (Explore, Browser); the pattern transfers, the implementations do not.
Relevance to tai
Validates tai's fork guidance ("fork background subagents for independent, long, or context-heavy work") as the same architecture Cursor ships. Suggests one refinement worth pricing later: letting context-heavy workers run on a cheaper pinned model instead of the session default.
Changelog
| Date | Change |
|---|---|
| 2026-10-18 | Initial dossier created. |