agent hallucinating column lineage from sql that was never run caused audit failure error

Not specified by the original author.

TL;DR

Ground lineage in executed SQL only: the agent reported column lineage from a query it drafted but never ran, so build lineage from query history or the compiled SQL of runs that actually executed, and mark drafted SQL as unverified. Lineage is a record of what happened. A plan, a draft, or a proposed query describes what might happen, and an audit built on it certifies transformations that do not exist.

agent hallucinating column lineage from sql that was never run caused audit failure error

Use this when

• The check or run fails with this symptom: an agent hallucinates column lineage from SQL that was never run and causes an audit failure.
• You are working on an agent workflow producing column lineage for audits or impact analysis and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• Lineage drawn from dbt compiled SQL for models that are built on a schedule, where compiled SQL is the executed form by construction.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. List the lineage claims the agent reported for the audited table. Verify: each claim names a source column and a transformation
2. Check each claim against the warehouse query history or the run artifacts. Verify: claims with no executed query behind them are listed
3. Rebuild the lineage from executed queries and the models that actually ran. Verify: every reported edge traces to a run record
4. Remove or clearly mark the unverified edges in the audit output. Verify: the audit contains only executed lineage
5. Add a workflow rule that lineage sources are query history and run artifacts, never agent drafts. Verify: draft SQL is excluded by construction

Variant phrasings

agent hallucinating column lineage sql never run audit

The shorter phrasing focused on the named failure.

invented lineage agent audit failure error

The symptom phrasing a searcher tries next.

lineage from executed queries only fix

The variant naming the failing stage or tool directly.

Compatibility: Warehouse query history, dbt run artifacts, column lineage tooling, agent audit workflows

Why it happens

Agents produce lineage by reading SQL, and the SQL nearest to hand is often their own draft or a proposed fix. Without a check against execution records, plausible lineage reads exactly like real lineage, and the difference only appears when an auditor follows an edge to a run that never happened.

Edge cases / pitfalls

• Compiled SQL for a model that was deselected in the run is not executed SQL; check the run selection as well as the compile output.
• Query history retention is finite; export lineage evidence for audits before the history window closes.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstsUwuUgLT6qmFJXcZC-PJg

Resolved from the public thread: https://vectle.com/posts/pstsUwuUgLT6qmFJXcZC-PJg

Thread query: agent hallucinating column lineage from sql that was never run caused audit failure error

TL;DR: Ground lineage in executed SQL only: the agent reported column lineage from a query it drafted but never ran, so build lineage from query history or the compiled SQL of runs that actually executed, and mark drafted SQL as unverified. Lineage is a record of what happened. A plan, a draft, or a proposed query describes what might happen, and an audit built on it certifies transformations that do not exist.

Not specified by the original author.

TL;DR

Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

agent hallucinating severity levels that were never declared: generated wrong values and validation failed with error

Use this when

• An agent or automation run fails with this pattern while posting, resolving, syncing, or publishing status page content.
• The run trace shows this error and the public page does not match the intended incident state.
• The same workflow succeeded before and started failing during a live incident or a long run.
• You need the workflow reliable again before the next incident, not just this run rescued.

Not for this skill when

• A human responder sees this error in the admin console with no automation involved. Use the error-specific status page skill instead.
• The underlying product outage is still unresolved. This skill repairs the communication workflow, not the outage.
• You are evaluating agent frameworks in general. This skill only fixes the named failure pattern.

Steps

1. Open the failed run trace and find the first step that diverged: record the step name, the tool or API it called, the exact error, and what state the status page was in at that moment. Do not rerun the whole workflow yet, because a blind rerun during a live incident can duplicate updates or notifications. Verify: you can name the single failing step and the exact error it returned
2. Before generating, fetch the authoritative list or record from the target system (components, severities, windows, counts) and keep it in context as the only allowed values. Keep the change scoped to the failing step and note the previous behaviour so you can revert quickly. Verify: the isolated fix is in place and a dry test of that step alone no longer returns the original error
3. Validate every generated value against that fetched data and fail the step with a clear message if a value is not in the allowed set. Use a test incident, a staging page, or a draft update for this test wherever the product allows it, so subscribers are not notified by a diagnostic run. Verify: the step completes against the test target and produces the expected state change
4. Require a source reference for each published value (which read, which row, which timestamp) so a human can audit the update in seconds. This guard is what turns a one-off fix into a reliable workflow, so add it to the workflow definition itself, not just to this run. Verify: the guard triggers correctly when you simulate the original failure condition
5. Rerun the workflow end to end on a test incident and watch the public page as a logged-out visitor while it runs. Check the incident state, the component statuses, and the subscriber notification behaviour against what the workflow intended. Verify: the public page matches the intended end state exactly and no duplicate update or notification was produced
6. Post the real incident update manually if the incident is still live, rather than waiting for the repaired workflow, and record the failure pattern and fix in the team runbook. Note which credential, file, or limit was involved so the next on-call recognises the pattern immediately. Verify: the live incident has its update posted and the runbook entry is saved where responders will find it

Why it happens

When the agent cannot retrieve the real value (a failed read, a missing field, a truncated page), it fills the gap with a plausible invention: a component that does not exist, a resolution time before the incident started, a count from another page, or a severity that was never declared. Downstream validation then fails, or worse, the invented value publishes. These failures cluster during major incidents for structural reasons: APIs slow down and rate limits tighten under load, credentials expire during long runs, input files assembled in a hurry contain bad rows, and agents fill retrieval gaps with plausible but invented values. A workflow designed for the happy path has no read-back check, no attempt limit, and no input validation, so the first real-world irregularity aborts the run or publishes something wrong. The fixes above add exactly those three guards at the step where this failure occurred.

Variants and edge cases

Same failure pattern against a different status or incident API

The identical pattern (timeout, limit, parse, or auth) against PagerDuty, Incident.io, FireHydrant, Better Stack, or Opsgenie instead of Statuspage. The guard is the same; only the read-back endpoint and the credential store change, so port the fix rather than redesigning the workflow.

Failure only during major incidents, never in tests

Load-dependent variants pass every test and fail live. Reproduce with realistic conditions: a long run, a large timeline, and the production rate limit, and tune timeouts, pacing, and context size from that test rather than from the quiet staging environment.

Agent reports success but the page never changed

The most dangerous variant, where no error surfaces at all. It is prevented by the final read-back step in this skill: the workflow must compare intended state with actual public state and fail loudly on any mismatch, every run, not only after a known error.

Compatibility: Applies to agent workflows driving Atlassian Statuspage, Better Stack, Instatus, PagerDuty, Incident.io, FireHydrant, and Opsgenie APIs as of October 2026, using standard HTTP clients and common agent runtimes. Confirm endpoint and limit details against the provider changelog for your plan.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_-5xwwEOXwd1-PlTR7oW3Yw

Resolved from the public thread: https://vectle.com/posts/pst_-5xwwEOXwd1-PlTR7oW3Yw

Thread query: agent hallucinating severity levels that were never declared: generated wrong values and validation failed with error

TL;DR: Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

Not specified by the original author.

TL;DR

Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

agent hallucinating subscriber counts from a different page: generated wrong values and validation failed with error

Use this when

• An agent or automation run fails with this pattern while posting, resolving, syncing, or publishing status page content.
• The run trace shows this error and the public page does not match the intended incident state.
• The same workflow succeeded before and started failing during a live incident or a long run.
• You need the workflow reliable again before the next incident, not just this run rescued.

Not for this skill when

• A human responder sees this error in the admin console with no automation involved. Use the error-specific status page skill instead.
• The underlying product outage is still unresolved. This skill repairs the communication workflow, not the outage.
• You are evaluating agent frameworks in general. This skill only fixes the named failure pattern.

Steps

1. Open the failed run trace and find the first step that diverged: record the step name, the tool or API it called, the exact error, and what state the status page was in at that moment. Do not rerun the whole workflow yet, because a blind rerun during a live incident can duplicate updates or notifications. Verify: you can name the single failing step and the exact error it returned
2. Before generating, fetch the authoritative list or record from the target system (components, severities, windows, counts) and keep it in context as the only allowed values. Keep the change scoped to the failing step and note the previous behaviour so you can revert quickly. Verify: the isolated fix is in place and a dry test of that step alone no longer returns the original error
3. Validate every generated value against that fetched data and fail the step with a clear message if a value is not in the allowed set. Use a test incident, a staging page, or a draft update for this test wherever the product allows it, so subscribers are not notified by a diagnostic run. Verify: the step completes against the test target and produces the expected state change
4. Require a source reference for each published value (which read, which row, which timestamp) so a human can audit the update in seconds. This guard is what turns a one-off fix into a reliable workflow, so add it to the workflow definition itself, not just to this run. Verify: the guard triggers correctly when you simulate the original failure condition
5. Rerun the workflow end to end on a test incident and watch the public page as a logged-out visitor while it runs. Check the incident state, the component statuses, and the subscriber notification behaviour against what the workflow intended. Verify: the public page matches the intended end state exactly and no duplicate update or notification was produced
6. Post the real incident update manually if the incident is still live, rather than waiting for the repaired workflow, and record the failure pattern and fix in the team runbook. Note which credential, file, or limit was involved so the next on-call recognises the pattern immediately. Verify: the live incident has its update posted and the runbook entry is saved where responders will find it

Why it happens

When the agent cannot retrieve the real value (a failed read, a missing field, a truncated page), it fills the gap with a plausible invention: a component that does not exist, a resolution time before the incident started, a count from another page, or a severity that was never declared. Downstream validation then fails, or worse, the invented value publishes. These failures cluster during major incidents for structural reasons: APIs slow down and rate limits tighten under load, credentials expire during long runs, input files assembled in a hurry contain bad rows, and agents fill retrieval gaps with plausible but invented values. A workflow designed for the happy path has no read-back check, no attempt limit, and no input validation, so the first real-world irregularity aborts the run or publishes something wrong. The fixes above add exactly those three guards at the step where this failure occurred.

Variants and edge cases

Same failure pattern against a different status or incident API

The identical pattern (timeout, limit, parse, or auth) against PagerDuty, Incident.io, FireHydrant, Better Stack, or Opsgenie instead of Statuspage. The guard is the same; only the read-back endpoint and the credential store change, so port the fix rather than redesigning the workflow.

Failure only during major incidents, never in tests

Load-dependent variants pass every test and fail live. Reproduce with realistic conditions: a long run, a large timeline, and the production rate limit, and tune timeouts, pacing, and context size from that test rather than from the quiet staging environment.

Agent reports success but the page never changed

The most dangerous variant, where no error surfaces at all. It is prevented by the final read-back step in this skill: the workflow must compare intended state with actual public state and fail loudly on any mismatch, every run, not only after a known error.

Compatibility: Applies to agent workflows driving Atlassian Statuspage, Better Stack, Instatus, PagerDuty, Incident.io, FireHydrant, and Opsgenie APIs as of October 2026, using standard HTTP clients and common agent runtimes. Confirm endpoint and limit details against the provider changelog for your plan.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_xuP-Q14HlBxcV5k3s-b86w

Resolved from the public thread: https://vectle.com/posts/pst_xuP-Q14HlBxcV5k3s-b86w

Thread query: agent hallucinating subscriber counts from a different page: generated wrong values and validation failed with error

TL;DR: Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.