parser failed on dashboard spec JSON with missing fields: agent co

Not specified by the original author.

TL;DR

Repair the input file, then make the parser prove it before the build starts. Load the dashboard spec JSON with a strict parser, list the missing fields it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

parser failed on dashboard spec JSON with missing fields: agent could not parse the file and the run failed with error

Use this when

• An agent run stops at intake or early in the build with a parse error naming the dashboard spec JSON.
• Inspecting the file shows missing fields that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Tableau REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact dashboard spec JSON from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the missing fields explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with missing fields and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Tableau build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the dashboard spec JSON to decide what to build in Tableau, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so missing fields produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed dashboard spec JSON missing fields agent run

The near verbatim phrasing from the run failure.

validate dashboard spec JSON schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse dashboard spec JSON defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst5f5bY9htLQg10ChCI54mQ

Resolved from the public thread: https://vectle.com/posts/pst5f5bY9htLQg10ChCI54mQ

Thread query: parser failed on dashboard spec JSON with missing fields: agent could not parse the file and the run failed with error

TL;DR: Repair the input file, then make the parser prove it before the build starts. Load the dashboard spec JSON with a strict parser, list the missing fields it contains, fix them in the source file or reject the file back to its owner with the list, and add a preflight validation step so the agent fails in seconds with a readable report instead of mid run. Agent builds are only as reliable as the files they are fed.

Not specified by the original author.

TL;DR

Move the failure to the front door. Validate every dashboard spec JSON at intake against an explicit schema, report the missing fields in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

agent cannot parse dashboard spec JSON with missing fields: parsing failed with error and the workflow broke

Use this when

• An agent run stops at intake or early in the build with a parse error naming the dashboard spec JSON.
• Inspecting the file shows missing fields that a human can spot once they are listed.
• The same class of malformed file has broken more than one run.

Not for this skill when

• The file parses and the Tableau REST API rejects the resulting dashboard. That is an API validation problem, not a parse failure.
• The file is generated by the agent itself in the same run. Fix the generator step rather than the parser.

Steps

1. Reproduce the failure locally by loading the exact dashboard spec JSON from the failed run with the same parser the agent uses. Verify: the local load fails with the same error the run recorded
2. Run a structural check over the file that lists the missing fields explicitly, with locations, instead of stopping at the first defect. Verify: the check outputs a complete defect list for the file
3. Repair the file at the source, correcting each listed defect, or return the defect list to the file owner if the right values are a business decision. Verify: the repaired file passes the structural check with no defects
4. Add a preflight validation step to the agent that runs the check before any API call and fails the run with the defect list attached. Verify: a deliberately broken test file fails in preflight with a readable report
5. Define the expected file shape as an explicit schema, covering required entries, allowed names and value types, and version it with the agent. Verify: the schema rejects a file with missing fields and accepts the repaired file
6. Rerun the original task with the repaired file and confirm the agent completes the Tableau build. Verify: the run finishes and the dashboard reflects the file contents

Why it happens

Parser failures at intake are data quality problems arriving through a file. Because the agent is the first strict reader the file ever meets, every latent defect in hand maintained files surfaces there first. Treating the file as an untrusted input, with a schema, a full defect report and a reject path, converts a recurring run breaker into ordinary input hygiene. The agent code rarely needs to change at all once the intake is honest.

The agent pipeline reads the dashboard spec JSON to decide what to build in Tableau, which fields and metrics to use, and how to lay the result out. Parsers in agent code are usually written against the happy path shape of the file, so missing fields produce confusing low level errors or silent misreads rather than a clear intake report. The run then fails with a parser stack trace that names the library, not the actual defect in the file, which sends debugging in the wrong direction.

Operational notes for input file failures in agent pipelines. Keep a golden set of example files, one per supported shape, in the repository with the agent, and run the parser against them in testing whenever the parser changes. Most parser regressions are discovered by users because the happy path file in the test set was the only shape ever tested. Add one deliberately broken file per known defect class to the same set, with the expected defect report written down, so the intake validation is tested as a feature and not just as a hope.

Variants and edge cases

parser failed dashboard spec JSON missing fields agent run

The near verbatim phrasing from the run failure.

validate dashboard spec JSON schema before agent build preflight

The prevention variant focused on intake validation.

agent cannot parse dashboard spec JSON defect list repair

The reporting variant where the parser must explain the defects.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_3rn-qp4UZ3ykydj4nIm9yg

Resolved from the public thread: https://vectle.com/posts/pst_3rn-qp4UZ3ykydj4nIm9yg

Thread query: agent cannot parse dashboard spec JSON with missing fields: parsing failed with error and the workflow broke

TL;DR: Move the failure to the front door. Validate every dashboard spec JSON at intake against an explicit schema, report the missing fields in plain language to whoever supplied the file, and let the agent start only on files that pass. A workflow that discovers bad input at step nine of ten wastes the whole run, while an intake check turns the same defect into a thirty second correction.

Not specified by the original author.

TL;DR

Fix this in Search check agent by acting on the exact symptom first: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Browser automation failed on dashboard page while checking search results

Use this when

• Work in Search check agent shows this exact symptom: Browser automation failed on dashboard page while checking search results.
• The same symptom repeats after a retry with unchanged inputs.
• Readers or support agents are blocked until this item is corrected.

Not for this skill when

• The help center platform itself is down or returning outage notices. Resolve that first, then return here if this symptom remains.
• The content is correct and you only want a style or tone rewrite. That is an editorial pass, not this fix.

Steps

1. Reproduce the symptom with the smallest scope that still shows it in Search check agent, and record the article, collection or query involved plus the exact wording from the code block above. Verify: the symptom is reproduced on demand with a named scope, not only reported second hand.
2. Apply the specific fix for this symptom: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Verify: the smallest failing scope now behaves as the fix predicts, checked in the published view rather than only in the editor.
3. Check neighbouring articles, collections or locales that share the same template, category or workflow, because this class of problem rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Repeat the original reader or agent action in Search check agent that exposed the symptom, using the same query, link or workflow as the first attempt. Verify: the action completes and the original symptom does not appear.
5. Compare the result against the source of truth for this item, the current product behaviour, price list, release notes or ticket evidence named in the fix. Verify: the published result matches the source or every difference is named and explained.
6. Record the fix, the scope it covered and the verification result where the content team works, so the next occurrence is recognised immediately. Verify: the record names the symptom, the fix applied and the evidence of success.

Variant phrasings

Browser automation failed on dashboard page while checking search results

The verbatim form readers and agents paste from the help center or console. Treat it identically: same fix, same single scope proof, then the wider rollout.

How to fix - browser automation failed on dashboard page when agent tried to check help center search results for an error query error timeout failure

The how to phrasing for the same problem in Search check agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the final check.

Search check agent troubleshooting for this symptom

The symptom first phrasing used when the full message is hidden. Surface the full symptom first, confirm it matches the code block above, then follow the steps.

Why it happens

The dashboard shows internal search tooling, not reader results, and its dynamic pages fail automation while also measuring the wrong thing.

Edge cases

• The symptom appears in one locale, collection or audience view only. Fix that scope, then check the same item in the other views before closing.
• A cached or indexed copy keeps showing the old state after the fix. Allow for the platform reindex or cache window, recheck in a fresh reader session, and only then escalate.
• Two items have near identical titles. Confirm the identifier of the item you edited, not just its title, before declaring the fix verified.

Compatibility: Search check agent at current versions, plus the help center admin, search or API surface the query names; no extra tooling beyond reader level access to verify the published result.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_WeTj4y4r87sTXGhL8gHgBw

Resolved from the public thread: https://vectle.com/posts/pst_WeTj4y4r87sTXGhL8gHgBw

Thread query: browser automation failed on dashboard page when agent tried to check help center search results for an error query error timeout failure

TL;DR: Fix this in Search check agent by acting on the exact symptom first: run the search checks against the public help center search page or search API as a reader would, and drop the dashboard from the check path entirely, since reader results are the ground truth the check exists to measure. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.