parser failed on avro schema with union types agent couldn't parse sc

Not specified by the original author.

TL;DR

Union types need union-aware handling: parse the Avro schema with a real Avro library rather than a hand-rolled JSON walker, represent unions explicitly in the agent's internal model as a list of branch types, and resolve the null branch as the default case. Agents that flatten unions into a single type will fail or misread every nullable field, since nullable in Avro is a union with null.

parser failed on avro schema with union types agent couldn't parse schema error

Use this when

• The check or run fails with this symptom: the agent's schema parsing breaks on Avro union types.
• You are working on an agent that cannot parse an Avro schema containing union types 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

• An Avro compatibility rejection at the registry, which is an evolution problem rather than a parsing problem instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Load the schema with the Avro library's own parser and confirm it accepts the document. Verify: the library parses the schema cleanly
2. Find where the agent's code assumes a field type is a plain string, since union types arrive as a list. Verify: the single-type assumptions are located
3. Update the internal model so a field type can be a single type or a list of branch types, with the record and array branches recursed into. Verify: the model represents unions explicitly
4. Add handling for the common two-branch pattern of a type plus null, including which branch supplies the default. Verify: nullable unions resolve with their defaults
5. Parse a set of real schemas from the registry, including nested unions, and confirm the agent produces the expected field model for each. Verify: the real schema set parses into correct models

Variant phrasings

parser failed avro schema union types agent

The verbatim Avro-union phrasing.

avro union null branch parse schema agent

The null-branch phrasing for nullable fields.

agent cannot parse avro schema nested unions

The nested-union phrasing for records inside unions.

Compatibility: Avro schemas with union types parsed by agents using fastavro or the Avro Python library

Why it happens

Avro encodes optionality and polymorphism as unions, so unions appear in nearly every real schema, often nested inside records and arrays. A parser written against simple schemas meets a union early and fails because its type model has no place to put a list of types.

Edge cases / pitfalls

• Unions cannot contain duplicate types or more than one unnamed branch of the same kind; validate that before blaming the agent.
• Keep the raw schema beside the parsed model so branch decisions stay auditable.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstS-Y-qysk0LG4zho7KJulQ

Resolved from the public thread: https://vectle.com/posts/pstS-Y-qysk0LG4zho7KJulQ

Thread query: parser failed on avro schema with union types agent couldn't parse schema error

TL;DR: Union types need union-aware handling: parse the Avro schema with a real Avro library rather than a hand-rolled JSON walker, represent unions explicitly in the agent's internal model as a list of branch types, and resolve the null branch as the default case. Agents that flatten unions into a single type will fail or misread every nullable field, since nullable in Avro is a union with null.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: give the agent a real Avro reader instead of a text parser, read the schema header from the file with that reader, convert the extracted schema to JSON for the metadata build, and record field names and types per table so a later parse failure names the field involved. 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 parser failed on production schema Avro file - metadata build stalled

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent parser failed on production schema Avro file - metadata build stalled.
• 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: give the agent a real Avro reader instead of a text parser, read the schema header from the file with that reader, convert the extracted schema to JSON for the metadata build, and record field names and types per table so a later parse failure names the field involved. 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

Avro files are binary with an embedded schema header, so text or JSON parsing fails on the first bytes and the metadata build stalls even though the schema is present and readable with the right reader.

Variant phrasings

agent parser failed on production schema Avro file - metadata build stalled

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 parser failed on production schema Avro file synthetic metadata build stalled

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/psthua64nwuRZMy4QrTxAQRA

Resolved from the public thread: https://vectle.com/posts/psthua64nwuRZMy4QrTxAQRA

Thread query: agent parser failed on production schema Avro file synthetic metadata build stalled

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: give the agent a real Avro reader instead of a text parser, read the schema header from the file with that reader, convert the extracted schema to JSON for the metadata build, and record field names and types per table so a later parse failure names the field involved. 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

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.