agent hallucinating chart types that cannot render the data: generated wron

Not specified by the original author.

TL;DR

Remove the agent freedom to invent reference values. Require every metric, field, owner and date the agent puts in the dashboard to come from a lookup result in the same run, a schema read, a metric catalog query, or a Tableau REST API list call, and validate the finished spec against those lookups before publishing. Hallucinated chart types that cannot render the data pass casual review because they look plausible, so only a mechanical existence check catches them reliably.

agent hallucinating chart types that cannot render the data URIs generated wrong values and validation failed with error

Use this when

• Validation or the Tableau REST API rejects the generated dashboard for referencing entries that do not exist.
• The generated spec contains plausible names that nobody on the team recognises.
• The agent was given a goal and little or no live schema or catalog context for the build.

Not for this skill when

• The agent used real names but assembled them into a wrong calculation. That is a logic error, reviewed differently.
• The referenced entries existed but were renamed after the build. That is a drift problem, handled by revalidation on publish.

Steps

1. Collect the rejected values from the validation output and check each one against the live system through the Tableau REST API or a warehouse schema read. Verify: every rejected value is confirmed absent from the live system
2. List the lookups the build actually needs, such as the metric catalog, the table schema, the user directory, or the allowed value lists, and add a retrieval step for each before generation. Verify: each category of reference value has a named lookup source
3. Change the generation step so reference values are copied from lookup results, and the agent must cite the lookup entry for each value it uses. Verify: a sample generated spec traces every reference value to a lookup result
4. Add a validation gate after generation that checks every reference value in the spec against the live lookups and fails the run with the offending values listed. Verify: a seeded spec containing an invented value fails at the gate
5. Where a requested item genuinely has no match, make the agent stop and report the gap instead of substituting a plausible alternative. Verify: a test request for a missing metric ends with a gap report, not an invented metric
6. Rerun the original build and confirm the dashboard passes validation and renders in Tableau. Verify: the run completes and every chart shows data

Why it happens

Hallucination in agent builds is a context problem before it is a model problem. Asked to produce a complete spec, the model completes it, inventing the missing references in the style of the real ones. Systems that separate retrieval from generation, and that validate references mechanically, get the fluency without the fiction. The gate matters most because human reviewers are worst at exactly this check, plausible names slide past tired eyes.

When the agent builds a dashboard for Tableau it must name real things, metrics, fields, people and ranges that exist in the target system. Language models fill gaps in the supplied context with statistically likely names, and dashboard vocabulary is generic enough that invented entries read as real. The failure surfaces at validation or, worse, after publishing, when a chart references chart types that cannot render the data and the build is rejected or silently shows nothing. Grounding turns each name into a retrieved fact instead of a guess.

Operational notes for grounding failures in agent pipelines. Keep the lookup results the agent used in the run artifacts, so a disputed dashboard can be audited back to exactly what the system of record said at build time. Disputes about invented values otherwise degenerate into opinion, because the live system may have changed since the run. The artifact also improves the next run, since recurring missing entries in the catalog become a visible backlog for the data team instead of a series of one off agent failures.

Variants and edge cases

agent invented chart types grounding lookup validation

The grounding phrasing for retrieval before generation.

dashboard spec validation references do not exist agent

The validation gate variant naming the existence check.

agent should stop and report missing metric not invent one

The behaviour variant for honest gap reporting.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Lviaz0Ivyaj8JIN36mfJ3A

Resolved from the public thread: https://vectle.com/posts/pst_Lviaz0Ivyaj8JIN36mfJ3A

Thread query: agent hallucinating chart types that cannot render the data URIs generated wrong values and validation failed with error

TL;DR: Remove the agent freedom to invent reference values. Require every metric, field, owner and date the agent puts in the dashboard to come from a lookup result in the same run, a schema read, a metric catalog query, or a Tableau REST API list call, and validate the finished spec against those lookups before publishing. Hallucinated chart types that cannot render the data pass casual review because they look plausible, so only a mechanical existence check catches them reliably.

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 component names that do not exist on the 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_HzZtw23JACAObxRjj98PEA

Resolved from the public thread: https://vectle.com/posts/pst_HzZtw23JACAObxRjj98PEA

Thread query: agent hallucinating component names that do not exist on the 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.