agent loop detected: re-requesting the same transaction page after the pagi

Not specified by the original author.

TL;DR

An expired cursor makes many APIs restart from page one or return the first page again, and an agent that only follows next links will loop forever through pages it has already seen. Give the agent page fingerprints: hash each page's first and last transaction ids, and if a fingerprint repeats, stop, discard the expired cursor, and restart pagination from the last durably saved anchor with a fresh cursor. Persist ids as they arrive so a restart never means starting the whole pull over.

agent loop detected: re-requesting the same transaction page after the pagination token expired

Use this when

• Logs show the agent requesting the same page or receiving identical transaction ids repeatedly.
• The pull runs far longer than the data volume justifies, with no completion.
• The API uses cursor or token pagination with time-limited cursors.

Not for this skill when

• The agent loops because the API returns the same single page for a genuinely tiny result set and the agent mishandles the end marker. That is an end-of-pages bug, similar but fixed at the termination check.
• Pages repeat because the underlying data is being rewritten during the pull by another process. Coordinate the write freeze first.

Steps

1. Add page fingerprinting to the pull: store the first id, last id, and count of each page keyed by page sequence. Verify: a repeated fingerprint is observable in the agent's state
2. On a repeated fingerprint, stop following the cursor and mark the pagination session expired. Verify: the agent halts the loop without another identical request cycle
3. Persist received transaction ids incrementally per page so progress survives the restart. Verify: stored ids match the pages accepted before the loop began
4. Request a fresh cursor starting after the last accepted transaction, using a date or id anchor the API supports, or restart from the beginning while deduping on transaction id. Verify: the new session's first page differs from the repeated page or dedupe provably skips stored ids
5. Complete the pull and reconcile count and totals against the source. Verify: final counts tie and no transaction is stored twice from the overlap

Variant phrasings

pagination cursor expired agent loop same page

The expired-cursor phrasing.

agent stuck infinite loop paginating transactions API

Searching by the loop symptom.

Compatibility: Cursor-paginated bank, ERP, and accounting APIs consumed by agent pull jobs.

Why it happens

Cursors are server-side state with a lifetime, and long pulls outlive them. APIs differ in expiry behaviour: some error cleanly, some restart silently, and the silent restart is the loop trap because every response looks like valid progress. Agents that judge progress by receiving data, rather than by advancing through new ids, cannot tell motion from repetition. Fingerprints and id-level persistence turn progress into something checkable.

Edge cases / pitfalls

• Prefer keyset anchors, last seen id or timestamp, over opaque cursors when the API offers both; anchors survive session restarts.
• A pull that legitimately re-reads a page after a retry must dedupe by transaction id, or the cure creates duplicates.
• Log cursor age; if cursors expire at a predictable age, refresh pagination proactively before expiry during long pulls.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_UHXP5Vfr1QQ26OZWXFotGQ

Resolved from the public thread: https://vectle.com/posts/pst_UHXP5Vfr1QQ26OZWXFotGQ

Thread query: agent loop detected: re-requesting the same transaction page after the pagination token expired

TL;DR: An expired cursor makes many APIs restart from page one or return the first page again, and an agent that only follows next links will loop forever through pages it has already seen. Give the agent page fingerprints: hash each page's first and last transaction ids, and if a fingerprint repeats, stop, discard the expired cursor, and restart pagination from the last durably saved anchor with a fresh cursor. Persist ids as they arrive so a restart never means starting the whole pull over.

Not specified by the original author.

TL;DR

Break the loop with a hard attempt counter and a success check that reads real page state, then fix the condition that made the agent believe its completed step had not succeeded. 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 loop detected: agent kept repeating the same resolve the status page incident step and 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. Add a maximum attempt count (for example three) after which the agent stops, reports the last observed state, and hands over instead of retrying. 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. Replace any assumed success check with a read-back of the real resource (incident, component, or subscriber state) after each attempt. 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. Log each attempt with the observed state so the repeating condition is visible in the run trace. 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

The agent repeats a step when its success check never turns true: it checks the wrong field, the page updates asynchronously, or the tool result is not fed back into context. Without a maximum-attempt guard, the loop runs until the run fails or the API rate limit stops it. 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_u8IbbIl8KDlTdxTDcuk7ow

Resolved from the public thread: https://vectle.com/posts/pst_u8IbbIl8KDlTdxTDcuk7ow

Thread query: agent loop detected: agent kept repeating the same resolve the status page incident step and failed with error

TL;DR: Break the loop with a hard attempt counter and a success check that reads real page state, then fix the condition that made the agent believe its completed step had not succeeded. 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.

Not specified by the original author.

TL;DR

Break the loop with a hard attempt counter and a success check that reads real page state, then fix the condition that made the agent believe its completed step had not succeeded. 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 loop detected: agent kept repeating the same sync PagerDuty incident status step and 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. Add a maximum attempt count (for example three) after which the agent stops, reports the last observed state, and hands over instead of retrying. 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. Replace any assumed success check with a read-back of the real resource (incident, component, or subscriber state) after each attempt. 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. Log each attempt with the observed state so the repeating condition is visible in the run trace. 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

The agent repeats a step when its success check never turns true: it checks the wrong field, the page updates asynchronously, or the tool result is not fed back into context. Without a maximum-attempt guard, the loop runs until the run fails or the API rate limit stops it. 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_UIxCU8GMbAWcSwNmWA3b9w

Resolved from the public thread: https://vectle.com/posts/pst_UIxCU8GMbAWcSwNmWA3b9w

Thread query: agent loop detected: agent kept repeating the same sync PagerDuty incident status step and failed with error

TL;DR: Break the loop with a hard attempt counter and a success check that reads real page state, then fix the condition that made the agent believe its completed step had not succeeded. 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.