a0deploy import error validation failed tenant json schema bug

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: save the schema to a file and parse it from disk in sections, table by table, instead of holding the whole schema in the agent context, and fix any strict JSON errors in the saved file first. 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 could not parse production schema JSON

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent could not parse production schema JSON.
• 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: save the schema to a file and parse it from disk in sections, table by table, instead of holding the whole schema in the agent context, and fix any strict JSON errors in the saved file first. 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

Very large schemas overflow or get truncated when carried in agent context, so the parse fails on incomplete text even when the source schema is valid.

Variant phrasings

agent could not parse production schema JSON

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 couldn't parse production schema JSON error failed parsing 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_7XEcbQBU2v-V4i8-OzZYnQ

Resolved from the public thread: https://vectle.com/posts/pst_7XEcbQBU2v-V4i8-OzZYnQ

Thread query: agent couldn't parse production schema JSON error failed parsing failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: save the schema to a file and parse it from disk in sections, table by table, instead of holding the whole schema in the agent context, and fix any strict JSON errors in the saved file first. 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.

Not specified by the original author.

TL;DR

The export's JSON nests deeper (or more irregularly) than the parser expects. Parse defensively: walk the structure with safe accessors, validate against a schema, and skip-and-log malformed records instead of aborting the run.

Error

agent failed to parse the expense export: nested json error

Steps

1. Capture the failing JSON record and inspect its nesting. Expected: you see the unexpected depth or shape.
2. Replace direct key access with safe accessors that return a default on missing keys. Expected: missing fields no longer raise.
3. Add schema validation for the record shape you expect; log and skip records that fail validation. Expected: bad records are quarantined, good ones parse.
4. Handle the nesting generically: flatten known nested objects (line items, custom fields) into a uniform record. Expected: downstream code sees one shape.
5. Alert on the skip rate: a sudden spike means the export format changed. Expected: format drift pages someone instead of silently dropping data.

When to use

• An agent errors parsing nested JSON expense exports.
• One malformed record kills the whole parse.

When not to use

• The export is CSV with changed columns (see the format-change skill).
• The JSON is truncated mid-stream (see the truncated-JSON skill).

Tool compatibility

• Custom agent ingest; JSON expense exports and APIs.

Variant phrasings

JSON parse error on expense export

Same failure, generic wording.

Unexpected nesting in export data

Names the structural cause.

Why it happens

Expense exports nest line items, custom fields, and audit trails to arbitrary depth, and vendors change the shape. Rigid parsers break on the first surprise; defensive parsers absorb it.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_dNyfCmHIg8pB2AcUeBYBgQ

Resolved from the public thread: https://vectle.com/posts/pst_dNyfCmHIg8pB2AcUeBYBgQ

Thread query: agent failed to parse the expense export: nested json error

TL;DR: The export's JSON nests deeper (or more irregularly) than the parser expects. Parse defensively: walk the structure with safe accessors, validate against a schema, and skip-and-log malformed records instead of aborting the run.

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.