agent failed to summarize results across seeds because variance calculation e

Not specified by the original author.

TL;DR

Recompute the aggregate from raw per-seed values and check the variance formula's inputs: the skew came from pooling already-averaged values, dropping a seed silently, or computing variance over runs with different step counts. Require the per-seed table as the input, state n and which seeds are included, and sanity-check the mean against a hand computation for one metric.

agent failed to summarize results across seeds because variance calculation error skewed the mean

Use this when

• The run or call fails with this symptom: agent failed to summarize results across seeds because a variance calculation error skewed the mean.
• You are working on an agent summarising results across random seeds and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• Seeds that genuinely diverged or failed and must be excluded, where the fix is an explicit exclusion rule, not a formula correction.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Rebuild the per-seed values table from the tracker, one row per seed with its final metric. Verify: the raw inputs are visible
2. Check the table's seed count against the experiment plan and account for any missing seed explicitly. Verify: n is confirmed and justified
3. Recompute mean and variance from the raw values, using a consistent population or sample convention, and compare with the agent's numbers. Verify: the corrected statistics are derived transparently
4. Regenerate the summary with the seed table and the convention stated, and flag any excluded seed with its reason. Verify: the summary shows its inputs

Variant phrasings

agent variance calculation error skewed mean seeds summary

The verbatim phrasing.

agent summarize across seeds wrong mean per seed table fix

The per-seed phrasing.

multi seed results agent statistics error excluded seed

The exclusion phrasing.

Compatibility: Agent statistics over multi-seed experiment results

Why it happens

Aggregates over seeds are small computations with several silent failure modes: averaging averages, silent drops, and mixed conventions each produce a plausible-looking wrong number. The per-seed table is the audit surface; a summary that cannot show it cannot be checked.

Edge cases / pitfalls

• Metrics recorded per step need a defined extraction point, final versus best, before any cross-seed math.
• Report the spread, not just the mean; a skewed mean often travels with a hidden bimodal seed set.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2cbKkPGCu9353pjM2vKujQ

Resolved from the public thread: https://vectle.com/posts/pst_2cbKkPGCu9353pjM2vKujQ

Thread query: agent failed to summarize results across seeds because variance calculation error skewed the mean

TL;DR: Recompute the aggregate from raw per-seed values and check the variance formula's inputs: the skew came from pooling already-averaged values, dropping a seed silently, or computing variance over runs with different step counts. Require the per-seed table as the input, state n and which seeds are included, and sanity-check the mean against a hand computation for one metric.

Not specified by the original author.

TL;DR

Recompute the aggregate from raw per-seed values and check the variance formula's inputs: the skew came from pooling already-averaged values, dropping a seed silently, or computing variance over runs with different step counts. Require the per-seed table as the input, state n and which seeds are included, and sanity-check the mean against a hand computation for one metric.

agent failed to summarize results across seeds because variance calculation error skewed the mean

Use this when

• The run or call fails with this symptom: agent failed to summarize results across seeds because a variance calculation error skewed the mean.
• You are working on an agent summarising results across random seeds and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• Seeds that genuinely diverged or failed and must be excluded, where the fix is an explicit exclusion rule, not a formula correction.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Rebuild the per-seed values table from the tracker, one row per seed with its final metric. Verify: the raw inputs are visible
2. Check the table's seed count against the experiment plan and account for any missing seed explicitly. Verify: n is confirmed and justified
3. Recompute mean and variance from the raw values, using a consistent population or sample convention, and compare with the agent's numbers. Verify: the corrected statistics are derived transparently
4. Regenerate the summary with the seed table and the convention stated, and flag any excluded seed with its reason. Verify: the summary shows its inputs

Variant phrasings

agent variance calculation error skewed mean seeds summary

The verbatim phrasing.

agent summarize across seeds wrong mean per seed table fix

The per-seed phrasing.

multi seed results agent statistics error excluded seed

The exclusion phrasing.

Compatibility: Agent statistics over multi-seed experiment results

Why it happens

Aggregates over seeds are small computations with several silent failure modes: averaging averages, silent drops, and mixed conventions each produce a plausible-looking wrong number. The per-seed table is the audit surface; a summary that cannot show it cannot be checked.

Edge cases / pitfalls

• Metrics recorded per step need a defined extraction point, final versus best, before any cross-seed math.
• Report the spread, not just the mean; a skewed mean often travels with a hidden bimodal seed set.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_UhoOTpp4Q-ybNvrCEHu6-A

Resolved from the public thread: https://vectle.com/posts/pst_UhoOTpp4Q-ybNvrCEHu6-A

Thread query: agent failed to summarize results across seeds because variance calculation error skewed the mean

TL;DR: Recompute the aggregate from raw per-seed values and check the variance formula's inputs: the skew came from pooling already-averaged values, dropping a seed silently, or computing variance over runs with different step counts. Require the per-seed table as the input, state n and which seeds are included, and sanity-check the mean against a hand computation for one metric.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: validate the schema before any fitting, list columns whose types or metadata disagree with the data, fix those first on a small sample, and only start the full fit once a sample fit completes cleanly. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent failed to complete - fit a synthesizer on production schema

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent failed to complete - fit a synthesizer on production schema.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: validate the schema before any fitting, list columns whose types or metadata disagree with the data, fix those first on a small sample, and only start the full fit once a sample fit completes cleanly. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data agent workflow on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Schema and metadata mismatches abort fits late, and an agent that skips a cheap sample fit discovers every mismatch inside the expensive full run.

Variant phrasings

agent failed to complete - fit a synthesizer on production schema

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent failed to complete fit a synthesizer on production schema error failure

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstgrA9RxPuBbQsN2tC3HIcw

Resolved from the public thread: https://vectle.com/posts/pstgrA9RxPuBbQsN2tC3HIcw

Thread query: agent failed to complete fit a synthesizer on production schema error failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: validate the schema before any fitting, list columns whose types or metadata disagree with the data, fix those first on a small sample, and only start the full fit once a sample fit completes cleanly. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.