agent failed to compare evidently drift reports across two dates afte

Not specified by the original author.

TL;DR

Align the two reports before comparing them: the agent failed to compare Evidently drift reports across dates after a parse error, so extract both reports through the same parser version, normalise metric names, and only then diff the drift results. Cross-date comparison fails when the two reports were produced or parsed differently. A report format change between the dates is the usual hidden difference.

agent failed to compare evidently drift reports across two dates after report parse error

Use this when

• The check or run fails with this symptom: an agent fails to compare Evidently drift reports across two dates after a report parse error.
• You are working on an agent workflow tracking Evidently drift over time for a model or a table 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

• Reports from the same day and version that still fail to parse, which points at a damaged report file.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Record the Evidently version that produced each report. Verify: the versions match, or the difference is named
2. Parse both reports with one extractor and export both to the same flat metric table. Verify: both dates produce rows in the identical shape
3. Normalise metric and column names across the two exports. Verify: the same drift metric has one name on both dates
4. Diff the flat tables by column and metric. Verify: drift changes between the dates are listed
5. Save the flat export with each report. Verify: the next comparison starts from parsed data, not from the reports

Variant phrasings

agent failed compare evidently drift reports two dates parse

The shorter phrasing focused on the named failure.

evidently drift report comparison parse error

The symptom phrasing a searcher tries next.

compare drift reports across dates fix

The variant naming the failing stage or tool directly.

Compatibility: Evidently report versions and JSON exports, agent comparison workflows

Why it happens

Evidently report internals evolve between versions, so a parser fitted to the newer report fails or misreads the older one. Comparison code that assumes one format across a version boundary breaks even though both reports are individually valid.

Edge cases / pitfalls

• Store the parsed metrics at report time; future format changes then never reach historical comparisons.
• A missing column in one date is a finding about the feature set, not a parse failure; keep absent and unparsable distinct in the output.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstqUHIdT80Sf9RQc34GUWNA

Resolved from the public thread: https://vectle.com/posts/pstqUHIdT80Sf9RQc34GUWNA

Thread query: agent failed to compare evidently drift reports across two dates after report parse error

TL;DR: Align the two reports before comparing them: the agent failed to compare Evidently drift reports across dates after a parse error, so extract both reports through the same parser version, normalise metric names, and only then diff the drift results. Cross-date comparison fails when the two reports were produced or parsed differently. A report format change between the dates is the usual hidden difference.

Not specified by the original author.

TL;DR

Fix this in the detector comparison by acting on the exact message first: align both detectors to the same start, end, and step before comparing, rerun the comparison extract with those bounds, and report the bounds alongside the result. 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.

when agent tried to compare detectors side by side agent failed error mismatched time ranges

Use this when

• The operation in the detector comparison fails with this exact message: when agent tried to compare detectors side by side agent failed error mismatched time ranges.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the detector comparison, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: align both detectors to the same start, end, and step before comparing, rerun the comparison extract with those bounds, and report the bounds alongside the result. Verify: both detectors score the identical window and differences reflect the models, not the ranges.
3. Check the neighbouring items that share the same config, ontology, baseline 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 the detector comparison 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, decisions or alerts against the source in the detector comparison 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

Detectors compared over different windows disagree because they saw different data; the mismatch looks like a model difference.

Variant phrasings

when agent tried to compare detectors side by side agent failed error mismatched time ranges

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

How to fix: when agent tried to compare detectors side by side agent failed error mismatched time ranges

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

the detector comparison 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: the detector comparison at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_BeHgX4ekR8wY1X8CcPl9XA

Resolved from the public thread: https://vectle.com/posts/pst_BeHgX4ekR8wY1X8CcPl9XA

Thread query: when agent tried to compare detectors side by side agent failed error mismatched time ranges

TL;DR: Fix this in the detector comparison by acting on the exact message first: align both detectors to the same start, end, and step before comparing, rerun the comparison extract with those bounds, and report the bounds alongside the result. 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

Compare the key types field by field and cast keys to one agreed type (for example string on both sides) at ingestion, because the drift check failed when the entity key is an integer in Hopsworks and a string in serving, so identical customers never match and the comparison reports phantom drift. Start with the Hopsworks feature group schema beside the serving layer schema for the same keys, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchanged.

agent failed to detect entity key type drift between Hopsworks and serving error comparison failed

Use this when

• You see this exact message or symptom: agent failed to detect entity key type drift between Hopsworks and serving error comparison failed
• One run, record, call, or object is affected and you can name it
• A retry without changing anything reproduces the same result

Not for this skill when

• The system fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once using the Hopsworks feature group schema beside the serving layer schema for the same keys, and record the time, the workspace or account, and the one item it names. Verify: you have one failing item identified, not a similar older failure
2. Inspect the declared key type in the feature group, the key type in the serving schema, and a sample key value encoded by each side. Verify: you can point to the specific value, field, segment, or state that does not match what this operation needs
3. Apply the fix at that source: Compare the key types field by field and cast keys to one agreed type (for example string on both sides) at ingestion, because the drift check failed when the entity key is an integer in Hopsworks and a string in serving, so identical customers never match and the comparison reports phantom drift. Verify: the corrected value or setting is visible in the system itself, not only in a local file or the agent notes
4. Re-run the original operation on the single affected item first. Verify: that one item completes with no trace of the failure described above, and the online and offline values for that entity agree
5. Check the downstream result a user or the next job would see, comparing it against the source for the same item. Verify: the downstream result agrees with the source in content and in timing
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the failure and the fix, and no earlier item regressed

Variant phrasings

agent failed to detect entity key type drift between Hopsworks and serving error comparison failed

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the item the log or record names.

Tool-first phrasing of this failure

The phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does this keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the input, setting, or record state, not in the attempt.

Compatibility: Hopsworks feature groups with external serving layers, key encoding checks.

Why it happens

Type drift sneaks in through a schema evolution on one side only. Values look identical when printed, which makes the mismatch invisible until keys are compared with their types attached.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstq6zSAKdUb8OoGTRX-tbUQ

Resolved from the public thread: https://vectle.com/posts/pstq6zSAKdUb8OoGTRX-tbUQ

Thread query: agent failed to detect entity key type drift between Hopsworks and serving error comparison failed

TL;DR: Compare the key types field by field and cast keys to one agreed type (for example string on both sides) at ingestion, because the drift check failed when the entity key is an integer in Hopsworks and a string in serving, so identical customers never match and the comparison reports phantom drift. Start with the Hopsworks feature group schema beside the serving layer schema for the same keys, work on one affected call, record, or run first, and prove the fix there before running the full set. Most cases clear once the cause named in step 2 is corrected at its source rather than retried unchang