agent hallucinating synthetic data values error fabricated records that

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: ground every reported value in a tool result, require the agent to cite the output file and row identifiers for any record it describes, and verify a spot sample of its claims against the file before accepting the report. 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 hallucinating synthetic data values - fabricated records

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent hallucinating synthetic data values - fabricated records.
• 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: ground every reported value in a tool result, require the agent to cite the output file and row identifiers for any record it describes, and verify a spot sample of its claims against the file before accepting the report. 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

When an agent reports generation results without re reading the output, it can invent plausible records that no run produced, and only file level verification catches it.

Variant phrasings

agent hallucinating synthetic data values - fabricated records

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 hallucinating synthetic data values error fabricated records that do not exist 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/pst_dKDSVv94Ns1yKZJTqDdBRQ

Resolved from the public thread: https://vectle.com/posts/pst_dKDSVv94Ns1yKZJTqDdBRQ

Thread query: agent hallucinating synthetic data values error fabricated records that do not exist failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: ground every reported value in a tool result, require the agent to cite the output file and row identifiers for any record it describes, and verify a spot sample of its claims against the file before accepting the report. 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.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: constrain the agent to the source schema field list, validate every field name in its output or plan against that list, and reject any step that references a field the schema does not contain. 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 hallucinating synthetic data fields - invented data not in source

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent hallucinating synthetic data fields - invented data not in source.
• 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: constrain the agent to the source schema field list, validate every field name in its output or plan against that list, and reject any step that references a field the schema does not contain. 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

Agents invent plausible sounding fields when working from a task description instead of the actual schema, and schema validation is the cheap check that stops it.

Variant phrasings

agent hallucinating synthetic data fields - invented data not in source

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 hallucinating synthetic data fields error invented data not in source 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/pst_1cCPRZL0tNcu39DjvVAevQ

Resolved from the public thread: https://vectle.com/posts/pst_1cCPRZL0tNcu39DjvVAevQ

Thread query: agent hallucinating synthetic data fields error invented data not in source failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: constrain the agent to the source schema field list, validate every field name in its output or plan against that list, and reject any step that references a field the schema does not contain. 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.

Not specified by the original author.

TL;DR

Ground every reported number in a fetched record: the agent invented runs because it generated the report's shape first and filled values from plausibility instead of queries. Require each reported run to carry an id that resolves on the tracker, verify a sample of the report's claims against live fetches, and block the report when any cited id fails to resolve.

agent hallucinating experiment results for runs that never existed and reporting failed metrics as passed

Use this when

• The run or call fails with this symptom: agent hallucinating experiment results for runs that never existed and reporting failed metrics as passed.
• You are working on an agent reporting experiment results to a team or a log 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

• A report over runs that were deleted after being fetched earlier, where the fix is labelling historical data rather than treating it as invented.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Extract every run id and metric value from the draft report and resolve each id against the tracker. Verify: unresolvable ids are listed
2. For resolved runs, compare the reported metrics and statuses with the tracker's actual values. Verify: value mismatches are listed separately from missing runs
3. Regenerate the report from fetched records only, with the fetch results, not the model's memory, as the data source. Verify: every claim in the new report maps to a fetched record
4. Add a verification pass that fails the report if any citation does not resolve, and run it before delivery. Verify: the gate runs and passes on the corrected report

Variant phrasings

agent hallucinated experiment results runs never existed

The verbatim phrasing.

agent reported failed metrics as passed invented runs

The false-pass phrasing.

agent experiment report not grounded verify run ids

The grounding phrasing.

Compatibility: Agent reporting pipelines that must cite tracker data for every claim

Why it happens

A language model asked for results will produce results-shaped text whether or not it looked anything up, and plausible numbers for plausible runs are its default output. Without a fetch-and-cite discipline enforced mechanically, invention is indistinguishable from reporting until someone checks an id.

Edge cases / pitfalls

• Status flips, failed reported as passed, are the most damaging invention; verify statuses even when ids resolve.
• Keep fetch snapshots with the report so later checks compare against what the agent saw, not a changed tracker.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_OYv3EAhPyq4mglVjAoCXNg

Resolved from the public thread: https://vectle.com/posts/pst_OYv3EAhPyq4mglVjAoCXNg

Thread query: agent hallucinating experiment results for runs that never existed and reporting failed metrics as passed

TL;DR: Ground every reported number in a fetched record: the agent invented runs because it generated the report's shape first and filled values from plausibility instead of queries. Require each reported run to carry an id that resolves on the tracker, verify a sample of the report's claims against live fetches, and block the report when any cited id fails to resolve.