3ee85c1bc5
AI orchestrators occasionally emit very large batches (one sub-
agent producing a whole section in a single batch_design call).
Existing multi-line regression only covered pretty-printed JSON
in SINGLE ops — nothing pinned behavior when N itself grows.
5 scenarios:
1. 100 sibling I() ops → all land, <5s wall-clock
2. 200 sibling ops → 2x node count, <10s (catches O(n²) regressions)
3. 30-level nested I() chain via parent_id threading
4. 250 mixed ops (50 sections × 4 children) with parent refs
5. Partial failure: 1 bogus parent_id among 100 good ops — good
ones still land (don't let one bad op poison the batch)
Observed: 250-op mixed batch completes in ~42ms on an M-series
machine. Budgets are "reasonable" (5s / 10s / 15s), not "fast" —
they're meant to catch O(n²) regressions in the DSL parser / tree
insert / save loop, not enforce a perf target.
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| agent-native@e1f90cab96 | ||
| pen-acp | ||
| pen-ai-skills | ||
| pen-core | ||
| pen-engine | ||
| pen-figma | ||
| pen-mcp | ||
| pen-react | ||
| pen-renderer | ||
| pen-sdk | ||
| pen-types | ||
| CLAUDE.md | ||