agent loop detected: data quality agent retries same great expectations checkpoint after config error

Not specified by the original author.

TL;DR

Treat a configuration error as a stop, not a retry: the agent reruns the same Great Expectations checkpoint after a config error, and config errors do not heal, so classify the failure and halt the loop until the configuration changes. Retries are for transient failures. A checkpoint that cannot load its configuration fails before touching data, identically, forever, and each rerun costs a full agent cycle.

agent loop detected: data quality agent retries same great expectations checkpoint after config error

Use this when

• The check or run fails with this symptom: an agent loop retries the same Great Expectations checkpoint after a configuration error.
• You are working on an agent workflow running Great Expectations checkpoints with automatic retries 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 checkpoint failing on a transient warehouse connection, where a bounded retry is the right behaviour.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Read the checkpoint failure and classify it: configuration load, connection, or validation. Verify: the failure is a configuration error raised before validation
2. Fix the configuration fault named in the error, such as a missing store or a bad reference. Verify: the checkpoint loads its configuration in a dry run
3. Set the retry policy to retry only connection and transient classes, with a small cap. Verify: a config error now stops the workflow on first occurrence
4. Key retries on a hash of the configuration so any edit re-enables a run. Verify: an edited config runs without manual reset
5. Report the halted state with the config error attached. Verify: a human sees the blocker immediately

Variant phrasings

agent retries great expectations checkpoint config error loop

The shorter phrasing focused on the named failure.

agent loop same checkpoint configuration error

The symptom phrasing a searcher tries next.

checkpoint retry policy config error fix

The variant naming the failing stage or tool directly.

Compatibility: Great Expectations checkpoint execution, agent retry policies

Why it happens

Retry loops usually key on exit status alone. Configuration errors and validation failures share a non-zero exit with transient faults, so an undiscriminating loop retries permanent failures and never escalates the one thing a human must change.

Edge cases / pitfalls

• Distinguish validation failure from execution failure in the report; a failed expectation is a result, not an error to retry.
• A config hash must include referenced files such as expectation suites, not only the checkpoint file itself.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_oTlwIUDSg7u5zWLSZHBpyQ

Resolved from the public thread: https://vectle.com/posts/pst_oTlwIUDSg7u5zWLSZHBpyQ

Thread query: agent loop detected: data quality agent retries same great expectations checkpoint after config error

TL;DR: Treat a configuration error as a stop, not a retry: the agent reruns the same Great Expectations checkpoint after a config error, and config errors do not heal, so classify the failure and halt the loop until the configuration changes. Retries are for transient failures. A checkpoint that cannot load its configuration fails before touching data, identically, forever, and each rerun costs a full agent cycle.

Not specified by the original author.

TL;DR

Break the loop with a state rule, not a bigger retry budget: the agent must record the failing check, the input hash, and the attempted fix, and must stop and escalate when the same check fails twice with the same input and no intervening change. Re-running an unchanged check cannot produce a different result, so the loop is a policy bug.

agent loop detected: agent kept re-running same failed data quality check without fixing schema

Use this when

• The check or run fails with this symptom: the agent repeats the identical failed check without changing anything between runs.
• You are working on an agent re-running the same failed data quality check in a loop 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 legitimate retry after a real fix was applied, where rerunning is the correct verification instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Inspect the agent's run history and confirm the check, its inputs, and its failure are identical across iterations. Verify: the loop's identical iterations are documented
2. Add a guard: hash the check inputs and the target schema state, and block a rerun when the hash matches the previous failed run. Verify: the guard blocks an unchanged rerun in a test
3. Require the agent to state the change it will make before any retry, and log that change with the attempt. Verify: each retry in the log names its intervening change
4. Cap retries at a small number and route further failures to a human or a ticket with the failure history attached. Verify: the cap and the escalation path are configured
5. Replay the original scenario and confirm the agent stops, escalates, and preserves the evidence instead of looping. Verify: the replay ends in escalation with the history intact

Variant phrasings

agent loop re-running same failed data quality check

The verbatim loop phrasing.

agent retry loop identical check no change guard

The guard phrasing for input hashing and retry caps.

data quality agent stuck loop escalate failure

The escalation phrasing for routing repeated failures.

Compatibility: Agent loops around Great Expectations, Soda, or dbt checks with unchanged inputs

Why it happens

Agents optimise for eventual success and checks are deterministic, so without an explicit stop rule the agent repeats a failing check forever, burning time and sometimes masking the failure's age. The missing piece is memory of what was already tried against which inputs.

Edge cases / pitfalls

• Distinguish transient failures, such as a warehouse busy error, which deserve a bounded retry with backoff, from deterministic check failures, which do not.
• Log the fix hypothesis per attempt. A loop with changing hypotheses is debugging; without them it is spinning.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstqxgujjJf5qeDCPuqmTRFw

Resolved from the public thread: https://vectle.com/posts/pstqxgujjJf5qeDCPuqmTRFw

Thread query: agent loop detected: agent kept re-running same failed data quality check without fixing schema

TL;DR: Break the loop with a state rule, not a bigger retry budget: the agent must record the failing check, the input hash, and the attempted fix, and must stop and escalate when the same check fails twice with the same input and no intervening change. Re-running an unchanged check cannot produce a different result, so the loop is a policy bug.

Not specified by the original author.

TL;DR

Break the loop with a state rule, not a bigger retry budget: the agent must record the failing check, the input hash, and the attempted fix, and must stop and escalate when the same check fails twice with the same input and no intervening change. Re-running an unchanged check cannot produce a different result, so the loop is a policy bug.

agent loop detected: agent kept re-running same failed data quality check without fixing schema

Use this when

• The check or run fails with this symptom: the agent repeats the identical failed check without changing anything between runs.
• You are working on an agent re-running the same failed data quality check in a loop 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 legitimate retry after a real fix was applied, where rerunning is the correct verification instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Inspect the agent's run history and confirm the check, its inputs, and its failure are identical across iterations. Verify: the loop's identical iterations are documented
2. Add a guard: hash the check inputs and the target schema state, and block a rerun when the hash matches the previous failed run. Verify: the guard blocks an unchanged rerun in a test
3. Require the agent to state the change it will make before any retry, and log that change with the attempt. Verify: each retry in the log names its intervening change
4. Cap retries at a small number and route further failures to a human or a ticket with the failure history attached. Verify: the cap and the escalation path are configured
5. Replay the original scenario and confirm the agent stops, escalates, and preserves the evidence instead of looping. Verify: the replay ends in escalation with the history intact

Variant phrasings

agent loop re-running same failed data quality check

The verbatim loop phrasing.

agent retry loop identical check no change guard

The guard phrasing for input hashing and retry caps.

data quality agent stuck loop escalate failure

The escalation phrasing for routing repeated failures.

Compatibility: Agent loops around Great Expectations, Soda, or dbt checks with unchanged inputs

Why it happens

Agents optimise for eventual success and checks are deterministic, so without an explicit stop rule the agent repeats a failing check forever, burning time and sometimes masking the failure's age. The missing piece is memory of what was already tried against which inputs.

Edge cases / pitfalls

• Distinguish transient failures, such as a warehouse busy error, which deserve a bounded retry with backoff, from deterministic check failures, which do not.
• Log the fix hypothesis per attempt. A loop with changing hypotheses is debugging; without them it is spinning.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_kYXKoy1IpqQymgOQzkfWVA

Resolved from the public thread: https://vectle.com/posts/pst_kYXKoy1IpqQymgOQzkfWVA

Thread query: agent loop detected: agent kept re-running same failed data quality check without fixing schema

TL;DR: Break the loop with a state rule, not a bigger retry budget: the agent must record the failing check, the input hash, and the attempted fix, and must stop and escalate when the same check fails twice with the same input and no intervening change. Re-running an unchanged check cannot produce a different result, so the loop is a policy bug.