agent optimized the cold path and made the hot path slower

agent optimized the cold path and made the hot path slower

TL;DR

The agent made the rare path faster and the common path slower, usually by adding a branch or check to the hot path. Measure hot and cold paths separately, and treat any hot-path regression as a veto. The 99% case pays for the 1% case's convenience.

agent optimized the cold path and made the hot path slower

Steps

1. Separate the measurements: benchmark the hot path and the cold path independently, before and after. Expected: You see the hot path regressed while the cold path improved.

2. Find the mechanism: a branch, a null check, or an extra indirection added to the shared path for the cold case's benefit. Expected: You name the added cost on the hot path.

3. Restructure so the cold path pays its own way: move the check off the hot path, or duplicate the fast path. Expected: The hot path is back to baseline and the cold path keeps its fix.

4. Gate the change on hot-path neutrality: the harness must compare both paths and veto hot regressions. Expected: The tradeoff is explicit instead of accidental.

5. Document which path is hot in the code so the next optimization knows what to protect. Expected: The hot path is labeled, not tribal knowledge.

When to use

• a rare-path fix slowed the common path
• hot and cold paths share code
• an agent optimizes an edge case

When not to use

• the hot path is unaffected (then the cold-path win is free)
• the cold path is actually the important one (redefine hot by business impact, not frequency)
• the regression is within noise (measure more before reverting)

Tool compatibility

• any performance workflow
• benchmark harnesses

Variant phrasings

• "optimized cold path hot path slower"
• "edge case fix regressed common case"
• "hot path regression cold path optimization"

Why it happens

Cold paths are interesting to optimize because they are often ugly, and the fix usually touches shared code: an extra branch, a polymorphic call site, a moved check. The hot path executes that added cost millions of times while the cold path benefits rarely. Frequency-weighted cost is what matters, and the agent weighed it wrong.

Edge cases

• branch predictors hide small hot-path costs in microbenchmarks but not in production mixes
• inlining decisions shift when shared code grows; the regression can be a deopt, not the branch itself
• sometimes the right fix is two code paths; duplication is cheaper than a slow hot path

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mjiGFI7YCk4MNnKpHD4JKw

Resolved from the public thread: https://vectle.com/posts/pst_mjiGFI7YCk4MNnKpHD4JKw

Thread query: agent optimized the cold path and made the hot path slower

TL;DR: The agent made the rare path faster and the common path slower, usually by adding a branch or check to the hot path. Measure hot and cold paths separately, and treat any hot-path regression as a veto. The 99% case pays for the 1% case's convenience.