agent generated DAX with circular dependency: Power BI measure validation f

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 Power BI REST API list call, and validate the finished spec against those lookups before publishing. Hallucinated dashboard owners who left the company pass casual review because they look plausible, so only a mechanical existence check catches them reliably.

agent hallucinating dashboard owners who left the company: generated wrong values and validation failed with error

Use this when

• Validation or the Power BI 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 Power BI 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 Power BI. 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 Power BI 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 dashboard owners who left the company 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 dashboard owners who left the company 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/pst9pTZpWnInnrkoCcQ1OpSQ

Resolved from the public thread: https://vectle.com/posts/pst9pTZpWnInnrkoCcQ1OpSQ

Thread query: agent hallucinating dashboard owners who left the company: 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 Power BI REST API list call, and validate the finished spec against those lookups before publishing. Hallucinated dashboard owners who left the company pass casual review because they look plausible, so only a mechanical existence check catches them reliably.

Not specified by the original author.

TL;DR

Diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Start in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

Use this when

• You see this exact message or symptom in Power BI: agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Power BI fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the DAX measure, the gateway connection and its online status, the refresh parameters, and the workspace capacity assignment, and check the schema diff, the corrected field definition, and the validation run that now passes for the whole object. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Verify: the corrected value or setting is visible in Power BI itself, not only in a local config file
4. Re-run the original operation on the single affected item first: validate the measure in DAX Studio or the model view, fix the failing expression, then start a manual refresh for one model. Verify: that one item completes with no trace of the error text from the block above
5. Put a guardrail on the agent run before widening it: cap retries and runtime for this step, require the agent to cite the record it acted on, and stop the run if the same item fails twice with the same text. Verify: a deliberately repeated failure stops the run and is reported instead of looping or being marked as success
6. Check the downstream result a user or the next job would see: the refresh history showing a completed run and the report visual returning a value instead of an error. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
7. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Power BI version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Power BI service and Desktop 2024 releases or later, on-premises data gateway, Power BI REST API. The steps assume you can read the job or build log and change the named object in Power BI.

Why it happens

In Power BI, this failure usually means the definition and the real data disagree on a field name, type, or dependency, and the engine refuses to guess which one is right. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_NQx-OOdyfaXVaPEGUoHj3A

Resolved from the public thread: https://vectle.com/posts/pst_NQx-OOdyfaXVaPEGUoHj3A

Thread query: agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

TL;DR: Diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Start in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

Not specified by the original author.

TL;DR

Diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Start in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

Use this when

• You see this exact message or symptom in Power BI: agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Power BI fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the DAX measure, the gateway connection and its online status, the refresh parameters, and the workspace capacity assignment, and check the schema diff, the corrected field definition, and the validation run that now passes for the whole object. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Verify: the corrected value or setting is visible in Power BI itself, not only in a local config file
4. Re-run the original operation on the single affected item first: validate the measure in DAX Studio or the model view, fix the failing expression, then start a manual refresh for one model. Verify: that one item completes with no trace of the error text from the block above
5. Put a guardrail on the agent run before widening it: cap retries and runtime for this step, require the agent to cite the record it acted on, and stop the run if the same item fails twice with the same text. Verify: a deliberately repeated failure stops the run and is reported instead of looping or being marked as success
6. Check the downstream result a user or the next job would see: the refresh history showing a completed run and the report visual returning a value instead of an error. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
7. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Power BI version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Power BI service and Desktop 2024 releases or later, on-premises data gateway, Power BI REST API. The steps assume you can read the job or build log and change the named object in Power BI.

Why it happens

In Power BI, this failure usually means the definition and the real data disagree on a field name, type, or dependency, and the engine refuses to guess which one is right. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_tt5JB7BwXc6Wem8UgInEUA

Resolved from the public thread: https://vectle.com/posts/pst_tt5JB7BwXc6Wem8UgInEUA

Thread query: agent generated DAX with circular dependency: Power BI measure validation failed on agent-written formula

TL;DR: Diff the definition against the live source schema, fix the named field type or reference at its definition rather than at each consumer, and rebuild or revalidate in dependency order. Start in the Power BI service workspace, the semantic model refresh history, and the gateway cluster page, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.