agent hallucinating freshness SLA values not in contract caused alert failure error

Not specified by the original author.

TL;DR

Read SLA values from the contract, never from memory: the agent invented freshness thresholds that are not in the data contract, so load the contract for the table, extract the stated SLA, and refuse to configure an alert the contract does not define. An invented SLA creates alerts nobody agreed to and misses breaches everybody did. The contract is the only source an agent may quote for a threshold.

agent hallucinating freshness SLA values not in contract caused alert failure error

Use this when

• The check or run fails with this symptom: an agent hallucinates freshness SLA values not in the contract and causes an alert failure.
• You are working on an agent workflow that configures freshness alerts from data contracts 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 table with no contract at all, where the correct action is escalating for a contract rather than configuring any SLA.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Open the data contract for the table and locate the freshness section. Verify: the stated SLA value, or its absence, is confirmed from the contract itself
2. Compare every SLA the agent configured against the contract. Verify: each invented or mismatched value is listed
3. Correct the alert configuration to the contract values. Verify: configured thresholds match the contract exactly
4. Where the contract has no freshness section, stop and raise the gap instead of estimating a value. Verify: the gap is reported with the table name
5. Add a workflow rule that SLA fields are copied from a contract lookup, never generated. Verify: generated configurations carry the contract reference

Variant phrasings

agent hallucinating freshness SLA values not in contract

The shorter phrasing focused on the named failure.

invented SLA alert configuration failure agent

The symptom phrasing a searcher tries next.

freshness SLA contract mismatch agent fix

The variant naming the failing stage or tool directly.

Compatibility: Data contracts in YAML or JSON, Soda and Monte Carlo alert configuration, agent configuration workflows

Why it happens

Agents fill gaps with plausible numbers, and an SLA such as four hours sounds reasonable for almost any table. When the contract says twenty four hours, or says nothing, the invented value produces false alerts or false calm, and the mismatch surfaces only during an incident.

Edge cases / pitfalls

• A contract can be stale; if the configured business expectation differs, the fix is a contract change with an owner, not an agent-side override.
• Copy the SLA with its units; contracts mix minutes and hours and a unit slip is indistinguishable from an invented value.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstIcuQPrinVs7yK-Znc0oqw

Resolved from the public thread: https://vectle.com/posts/pstIcuQPrinVs7yK-Znc0oqw

Thread query: agent hallucinating freshness SLA values not in contract caused alert failure error

TL;DR: Read SLA values from the contract, never from memory: the agent invented freshness thresholds that are not in the data contract, so load the contract for the table, extract the stated SLA, and refuse to configure an alert the contract does not define. An invented SLA creates alerts nobody agreed to and misses breaches everybody did. The contract is the only source an agent may quote for a threshold.

Not specified by the original author.

TL;DR

Recompute freshness from the newest event timestamp in the source and a live read of the online value for the same entity, and discard any audit conclusion drawn from the job schedule, because a schedule says when a job should have run, not what data actually landed or is being served. Start with the actual online values and source max timestamps, not the schedule the agent cited, 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 hallucinating feature freshness from job schedule instead of actual values error audit failed

Use this when

• You see this exact message or symptom: agent hallucinating feature freshness from job schedule instead of actual values error audit 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 actual online values and source max timestamps, not the schedule the agent cited, 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 schedule the agent relied on, the max event timestamp per view, and one entity read from both stores with timestamps shown. 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: Recompute freshness from the newest event timestamp in the source and a live read of the online value for the same entity, and discard any audit conclusion drawn from the job schedule, because a schedule says when a job should have run, not what data actually landed or is being served. 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 hallucinating feature freshness from job schedule instead of actual values error audit 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: Agent run freshness audits, any feature store with scheduled materialization.

Why it happens

Schedules survive outages, paused DAGs, and empty partitions. An audit grounded in the timetable reports green while the data is stale, which is worse than no audit because it closes the investigation.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstgSmlAvQFmVH-zlLOhxaEA

Resolved from the public thread: https://vectle.com/posts/pstgSmlAvQFmVH-zlLOhxaEA

Thread query: agent hallucinating feature freshness from job schedule instead of actual values error audit failed

TL;DR: Recompute freshness from the newest event timestamp in the source and a live read of the online value for the same entity, and discard any audit conclusion drawn from the job schedule, because a schedule says when a job should have run, not what data actually landed or is being served. Start with the actual online values and source max timestamps, not the schedule the agent cited, 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.

Not specified by the original author.

TL;DR

Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

agent hallucinating maintenance windows in the wrong timezone: generated wrong values and validation failed with error

Use this when

• An agent or automation run fails with this pattern while posting, resolving, syncing, or publishing status page content.
• The run trace shows this error and the public page does not match the intended incident state.
• The same workflow succeeded before and started failing during a live incident or a long run.
• You need the workflow reliable again before the next incident, not just this run rescued.

Not for this skill when

• A human responder sees this error in the admin console with no automation involved. Use the error-specific status page skill instead.
• The underlying product outage is still unresolved. This skill repairs the communication workflow, not the outage.
• You are evaluating agent frameworks in general. This skill only fixes the named failure pattern.

Steps

1. Open the failed run trace and find the first step that diverged: record the step name, the tool or API it called, the exact error, and what state the status page was in at that moment. Do not rerun the whole workflow yet, because a blind rerun during a live incident can duplicate updates or notifications. Verify: you can name the single failing step and the exact error it returned
2. Before generating, fetch the authoritative list or record from the target system (components, severities, windows, counts) and keep it in context as the only allowed values. Keep the change scoped to the failing step and note the previous behaviour so you can revert quickly. Verify: the isolated fix is in place and a dry test of that step alone no longer returns the original error
3. Validate every generated value against that fetched data and fail the step with a clear message if a value is not in the allowed set. Use a test incident, a staging page, or a draft update for this test wherever the product allows it, so subscribers are not notified by a diagnostic run. Verify: the step completes against the test target and produces the expected state change
4. Require a source reference for each published value (which read, which row, which timestamp) so a human can audit the update in seconds. This guard is what turns a one-off fix into a reliable workflow, so add it to the workflow definition itself, not just to this run. Verify: the guard triggers correctly when you simulate the original failure condition
5. Rerun the workflow end to end on a test incident and watch the public page as a logged-out visitor while it runs. Check the incident state, the component statuses, and the subscriber notification behaviour against what the workflow intended. Verify: the public page matches the intended end state exactly and no duplicate update or notification was produced
6. Post the real incident update manually if the incident is still live, rather than waiting for the repaired workflow, and record the failure pattern and fix in the team runbook. Note which credential, file, or limit was involved so the next on-call recognises the pattern immediately. Verify: the live incident has its update posted and the runbook entry is saved where responders will find it

Why it happens

When the agent cannot retrieve the real value (a failed read, a missing field, a truncated page), it fills the gap with a plausible invention: a component that does not exist, a resolution time before the incident started, a count from another page, or a severity that was never declared. Downstream validation then fails, or worse, the invented value publishes. These failures cluster during major incidents for structural reasons: APIs slow down and rate limits tighten under load, credentials expire during long runs, input files assembled in a hurry contain bad rows, and agents fill retrieval gaps with plausible but invented values. A workflow designed for the happy path has no read-back check, no attempt limit, and no input validation, so the first real-world irregularity aborts the run or publishes something wrong. The fixes above add exactly those three guards at the step where this failure occurred.

Variants and edge cases

Same failure pattern against a different status or incident API

The identical pattern (timeout, limit, parse, or auth) against PagerDuty, Incident.io, FireHydrant, Better Stack, or Opsgenie instead of Statuspage. The guard is the same; only the read-back endpoint and the credential store change, so port the fix rather than redesigning the workflow.

Failure only during major incidents, never in tests

Load-dependent variants pass every test and fail live. Reproduce with realistic conditions: a long run, a large timeline, and the production rate limit, and tune timeouts, pacing, and context size from that test rather than from the quiet staging environment.

Agent reports success but the page never changed

The most dangerous variant, where no error surfaces at all. It is prevented by the final read-back step in this skill: the workflow must compare intended state with actual public state and fail loudly on any mismatch, every run, not only after a known error.

Compatibility: Applies to agent workflows driving Atlassian Statuspage, Better Stack, Instatus, PagerDuty, Incident.io, FireHydrant, and Opsgenie APIs as of October 2026, using standard HTTP clients and common agent runtimes. Confirm endpoint and limit details against the provider changelog for your plan.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mxmajVXtY9gRlU8MWlL0wA

Resolved from the public thread: https://vectle.com/posts/pst_mxmajVXtY9gRlU8MWlL0wA

Thread query: agent hallucinating maintenance windows in the wrong timezone: generated wrong values and validation failed with error

TL;DR: Ground every generated value in a live read of the target system, validate generated names, times, counts, and levels against that read, and reject the run output if any value cannot be traced to source data. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.