dbt cloud API rate limit hit mid-test execution workflow failure

Not specified by the original author.

TL;DR

Pace the agent's dbt Cloud API usage as a client of a shared service: batch status polls, cache job definitions, and spread test submissions instead of firing them together when a workflow fans out. When a limit lands mid-execution, the agent should wait for the window and resume polling the runs it already submitted, since the runs themselves continue server-side.

dbt cloud API rate limit hit mid-test execution workflow failure

Use this when

• The check or run fails with this symptom: test execution stalls after the dbt Cloud API begins limiting the agent.
• You are working on an agent hitting the dbt Cloud API limit during test execution 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

• A local dbt Core test run, where no cloud API limit applies instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Capture the limit response and count how many runs the workflow had submitted and how many it was polling. Verify: the limit context and run counts are recorded
2. Audit the agent's polling interval and call volume per run to find the amplification, usually many runs polled at a fixed short interval. Verify: the polling amplification is quantified
3. Implement adaptive polling: a longer interval as runs age, plus a single batched status check where the API allows. Verify: polling is adaptive and batched
4. On a limit response, back off for the stated window and resume polling existing runs rather than resubmitting them. Verify: limit handling resumes instead of resubmitting
5. Run the full test execution workflow and confirm it completes within the API budget with all runs reported. Verify: the workflow completes with every run's outcome

Variant phrasings

dbt cloud API rate limit mid test execution agent

The verbatim dbt Cloud limit phrasing.

dbt cloud api polling interval rate limit workflow

The polling phrasing for adaptive intervals.

agent dbt cloud limit resume polling existing runs

The resume phrasing for server-side runs.

Compatibility: dbt Cloud API used by agents to submit and poll test execution jobs

Why it happens

Agent workflows fan out: many runs, each polled frequently, multiply into request volumes the API's limits were not sized for. The runs are safe server-side; it is the agent's chatter that gets limited, so the cure is quieter, resumable polling.

Edge cases / pitfalls

• Submit runs with idempotency in mind: record each run identifier before polling so nothing is submitted twice.
• Prefer webhooks or completion callbacks over polling where dbt Cloud offers them.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_kZtHLLfZf9YZgYnLoVqZNg

Resolved from the public thread: https://vectle.com/posts/pst_kZtHLLfZf9YZgYnLoVqZNg

Thread query: dbt cloud API rate limit hit mid-test execution workflow failure

TL;DR: Pace the agent's dbt Cloud API usage as a client of a shared service: batch status polls, cache job definitions, and spread test submissions instead of firing them together when a workflow fans out. When a limit lands mid-execution, the agent should wait for the window and resume polling the runs it already submitted, since the runs themselves continue server-side.

Not specified by the original author.

TL;DR

Fix this in the DataGrail workflow step by doing this first: pace calls to the DataGrail API under its published limit, honour the wait guidance in its responses, queue the remaining calls, and resume the workflow from the last completed item once the limit window resets. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

DataGrail API rate limit hit mid-workflow

Use this when

• The operation fails with this exact problem: DataGrail API rate limit hit mid-workflow.
• The same failure repeats on retry with unchanged inputs.
• An audit, review, or request is blocked until this item completes.

Not for this skill when

• The message is a platform outage notice or a general sign-in failure. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the DataGrail workflow step, and copy the full message plus the item, account, or request involved into the run notes. Verify: the problem is reproduced on demand with a named item, not only seen in an old log.
2. Apply the specific fix for this problem: pace calls to the DataGrail API under its published limit, honour the wait guidance in its responses, queue the remaining calls, and resume the workflow from the last completed item once the limit window resets. Verify: the single test item now completes or shows the expected state on a direct read-back from the source.
3. Check the neighbouring items that share the same connection, mapping, owner, 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. Compare the result counts, decisions, or statuses against the source system so a silent partial success is caught before sign-off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
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 problem, the fix applied, and the evidence of success.

Why it happens

The workflow bursts calls across every record at once and exhausts the DataGrail quota, so later calls fail even though the workflow itself is sound.

Variant phrasings

DataGrail API rate limit hit mid-DSR fulfilment workflow error failed quota

The verbatim form searchers paste from the console or ticket. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix this failure in practice

The how-to phrasing for the same failure. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Troubleshooting the symptom-first form

The 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: the DataGrail workflow step at current versions, plus the source systems the query names; no extra tooling beyond the platform console or API access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_6VcQDKGAEDoc80CkslqwkQ

Resolved from the public thread: https://vectle.com/posts/pst_6VcQDKGAEDoc80CkslqwkQ

Thread query: DataGrail API rate limit hit mid-DSR fulfilment workflow error failed quota

TL;DR: Fix this in the DataGrail workflow step by doing this first: pace calls to the DataGrail API under its published limit, honour the wait guidance in its responses, queue the remaining calls, and resume the workflow from the last completed item once the limit window resets. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in SDV by acting on the exact message first: have the agent wait for the retry delay the API returns, fetch the evaluation results once and cache them to disk, reduce polling frequency for the rest of the workflow, and resume from the cached job identifier rather than rerunning the evaluation to get its results. 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.

API rate limit hit mid synthetic workflow - agent could not fetch evaluation results

Use this when

• The operation in SDV fails with this exact message or symptom: API rate limit hit mid synthetic workflow - agent could not fetch evaluation results.
• 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 SDV, 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: have the agent wait for the retry delay the API returns, fetch the evaluation results once and cache them to disk, reduce polling frequency for the rest of the workflow, and resume from the cached job identifier rather than rerunning the evaluation to get its results. 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 SDV 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 SDV 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

Evaluation result endpoints count against the same quota as generation calls, and an agent polling or refetching results in a loop spends that quota and gets limited exactly when it needs the final report.

Variant phrasings

API rate limit hit mid synthetic workflow - agent could not fetch evaluation results

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 - SDV API rate limit hit mid synthetic workflow agent could not fetch evaluation results

The how-to phrasing for the same failure in SDV. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

SDV 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: SDV 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_QXb5e4tf3DDv4v-llSkGMg

Resolved from the public thread: https://vectle.com/posts/pst_QXb5e4tf3DDv4v-llSkGMg

Thread query: SDV API rate limit hit mid synthetic workflow agent could not fetch evaluation results

TL;DR: Fix this in SDV by acting on the exact message first: have the agent wait for the retry delay the API returns, fetch the evaluation results once and cache them to disk, reduce polling frequency for the rest of the workflow, and resume from the cached job identifier rather than rerunning the evaluation to get its results. 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.