agent hallucinating department names that do not exist: generated wron

Not specified by the original author.

TL;DR

Constrain the agent to source values only and validate every invented-prone field against the system of record before it writes. The agent generated department names that do not exist because a required field was missing or ambiguous in its input and it filled the gap plausibly instead of stopping. Ground the step with a direct lookup, reject any value not traceable to the source, and make missing data a halt with a clear request, never a creative writing exercise.

agent hallucinating department names that do not exist: generated wrong values and validation failed

Use this when

• The agent output contains values that appear in no source record, such as invented assignments, people, or stock items.
• Failures happen on hires whose source records have missing or unusual fields the agent must interpret.
• Validation in the target rejects the generated values, or worse, occasionally accepts them.

Not for this skill when

• The source record itself holds the wrong value entered by a person. Correct the source data process instead.
• The agent copies a correct value into the wrong field. That is a mapping bug, not hallucination.

Steps

1. Capture the failing case completely: the agent input record, the exact values it generated, and its reasoning trace if available, and mark each generated value as present in the source, derived by a stated rule, or invented. Verify: every generated value is classified with evidence
2. Find the gap that invited invention: the missing, blank, or ambiguous source field the agent needed for this step, such as an absent assignment list, an unnamed approver, or an unchecked availability. Verify: the triggering gap is identified in the input record
3. Ground the step: give the agent a lookup tool against the authoritative list in ServiceNow, for example the real assignment catalogue, people directory, or inventory, and require it to select only values the lookup returns. Free-text generation for these fields is disabled by the step design. Verify: the step can only emit values returned by the lookup in a test with a missing field
4. Add a validation gate before any write: each critical value is checked against the source record or the lookup result, and any mismatch or untraceable value halts the step with a specific message naming the field and the hire. Verify: the gate blocks a seeded invented value in testing
5. Change the missing-data behaviour in the agent instructions and step logic: when a required value is absent, the agent stops that hire, records the exact missing field, and queues the hire for a human data fix, then continues with other hires. Verify: a test hire with a missing field is queued with the field named, not invented around
6. Re-run the affected hires once their source gaps are fixed, confirm the create the ServiceNow onboarding ticket step uses only traceable values, and audit recent completed runs for silently accepted invented values that need correction in ServiceNow. Verify: the audit finds no remaining untraceable values or lists them as owned cleanups

Why it happens

Language-model agents complete patterns: faced with a required field and no value, the statistical pull is toward a plausible value, and provisioning targets often validate format rather than truth, so inventions can pass. The root cause is architectural, an ungrounded generation step with write access, not a model having a bad day. Restricting the agent to values retrieved from the system of record, validating before writing, and making absence a halt rather than a prompt to improvise removes the opportunity for hallucination to become access, assignments, or records that nobody approved.

Variants and edge cases

Invented values pass target validation

Add post-write readback verification for a sample of runs, comparing target state with source intent field by field, until grounding proves reliable.

Agent invents only under time pressure prompts

Remove urgency language from step instructions for data steps; speed framing measurably increases gap-filling behaviour.

Lookup itself returns stale data

Grounding is only as good as the catalogue. Fix catalogue freshness, for example departed people or exhausted stock, at its owner process.

Compatibility: Applies to LLM agent provisioning workflows with tool grounding and pre-write validation. Requires agent workflow editor access.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_TJ-DBBDKdXzIFH-JlHGsbQ

Resolved from the public thread: https://vectle.com/posts/pst_TJ-DBBDKdXzIFH-JlHGsbQ

Thread query: agent hallucinating department names that do not exist: generated wrong values and validation failed with error

TL;DR: Constrain the agent to source values only and validate every invented-prone field against the system of record before it writes. The agent generated department names that do not exist because a required field was missing or ambiguous in its input and it filled the gap plausibly instead of stopping. Ground the step with a direct lookup, reject any value not traceable to the source, and make missing data a halt with a clear request, never a creative writing exercise.

Not specified by the original author.

TL;DR

Fix this in response rate calculation agent by acting on the exact problem first: fill every department rate only from retrieved invited and responded counts, show both counts beside each rate, recompute each rate from those counts independently, and reject any rate whose department or counts appear in no retrieved record. Do not rerun the same survey, report or export unchanged, because the same inputs produce the same failure. Prove the fix on one survey, team or department first, then rerun the full scope and reconcile the results.

agent hallucinating answers not in source data for calculate response rates by department error hallucination failure

Use this when

• The operation in response rate calculation agent fails with this exact problem: agent hallucinating answers not in source data for calculate response rates by department error hallucination failure.
• The same failure repeats on retry with unchanged inputs.
• A survey launch, results dashboard, manager report or action plan is blocked until this is resolved.

Not for this skill when

• The notice is a platform outage from the vendor. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with an interpretation call such as which driver to prioritise. That is a people analytics judgement, not this failure.

Steps

1. Reproduce the failure on one named survey, team or department in response rate calculation agent and record the full problem text plus the survey, team or period involved. Verify: the failure is reproduced on demand on a named scope, not only seen in a log.
2. Apply the specific fix for this problem: fill every department rate only from retrieved invited and responded counts, show both counts beside each rate, recompute each rate from those counts independently, and reject any rate whose department or counts appear in no retrieved record. Verify: the single failing survey, team or department now behaves as the fix predicts, or the corrected value is visible on the record.
3. Check neighbouring surveys, teams or departments that share the same distribution list, roster sync or dashboard, because this class of failure rarely affects only one group. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in response rate calculation agent on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
5. Compare the result against the source of truth, the raw response record, the HR roster record or the survey platform record, so a silent partial success is caught. Verify: values match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the survey run log so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied and the evidence of success.

Variant phrasings

agent hallucinating answers not in source data for calculate response rates by department error hallucination failure

The verbatim form searchers paste from the console, report or run log. Treat it identically: same fix, same single group proof, then the full rerun.

How to fix - agent hallucinating answers not in source data for calculate response rates by department error hallucination failure

The how to phrasing for the same failure in response rate calculation agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

response rate calculation agent troubleshooting for this failure

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

Why it happens

An agent can report a plausible rate for a department it never retrieved counts for, and independent recomputation from the two counts is what catches an invented rate before it is shared with leaders.

Edge cases

• The survey was reopened, the roster was resynced or a team was restructured after the failing run started. Refresh the survey state and reapply the fix against the current values, not the values from the failed run.
• The single group proof passes but the full rerun still fails on other groups. Treat each remaining failure as its own scope, because a shared distribution list or dashboard can hide more than one cause.
• The source systems disagree on the correct value. The raw response record governs response counts, the HR roster governs who was invited, and the platform record governs what was sent or shown; record which source won and why.

Compatibility: response rate calculation agent at current versions, plus the survey platform, roster or reporting tool the query names; no extra tooling beyond access to the source records needed to verify results.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_bwC-U013TEtoofQFY9DLTA

Resolved from the public thread: https://vectle.com/posts/pst_bwC-U013TEtoofQFY9DLTA

Thread query: agent hallucinating answers not in source data for calculate response rates by department error hallucination failure

TL;DR: Fix this in response rate calculation agent by acting on the exact problem first: fill every department rate only from retrieved invited and responded counts, show both counts beside each rate, recompute each rate from those counts independently, and reject any rate whose department or counts appear in no retrieved record. Do not rerun the same survey, report or export unchanged, because the same inputs produce the same failure. Prove the fix on one survey, team or department first, then rerun the full scope and reconcile the results.

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.