agent loop detected: agent kept re-querying the same finished run and failed

Not specified by the original author.

TL;DR

Stop the loop by making repetition visible and expensive. Log a hash of the step inputs and the tool result, abort when the same call repeats with the same result beyond a small budget, and require the agent to read back the Metabase state before attempting the step again. Loops during pull Metabase query results almost always mean a success signal the agent cannot see, so it retries a step that already worked or can never work.

agent loop detected: agent kept repeating the same pull Metabase query results step and failed with error

Use this when

• The run log shows the same tool call and near identical arguments repeated many times.
• The Metabase content shows repeated partial attempts or many duplicate objects from one run.
• The run ends by budget exhaustion rather than by a step error.

Not for this skill when

• The agent is slow but each step differs and progress is visible. That is a performance problem, not a loop.
• The agent failed once on a clear API error and stopped. Handle that error directly.

Steps

1. Freeze the run and extract its tool call sequence, counting repeats of the pull Metabase query results step and comparing their arguments. Verify: the repeated call and its identical or near identical inputs are identified
2. Inspect the Metabase state the step was trying to change and compare it with what the agent believed, using the API to read the current card and dashboard definitions plus the query result payloads. Verify: you can state whether the step had already succeeded, was failing permanently, or was aimed at the wrong target
3. Fix the root mismatch, such as pointing verification at the object the build actually creates, or teaching the step to treat an already exists result as success. Verify: a single manual pass of the step now reports success correctly
4. Add a repetition guard to the agent, hashing each tool call with its result and stopping the run after a small number of identical repeats with a clear loop error. Verify: a test run with a forced failure stops at the guard instead of exhausting its budget
5. Add a per step attempt budget and a run level step budget sized for the dashboard build, with the budget breach reported as its own failure type. Verify: the run summary separates loop aborts from ordinary step failures
6. Rerun the full pull Metabase query results task and confirm the agent completes in a normal step count. Verify: the run finishes and the dashboard opens in Metabase

Why it happens

Loops are a state recognition failure. The agent acts, the world changes or refuses to change, and the agent cannot tell the difference from its observations, so its planner rationally chooses the same action again. Retries feel safe to a planner because they are reversible, which is exactly why an external guard has to count them. Progress detection, where each step must change an observable state marker, is the general fix across agent frameworks.

Dashboard building agents plan, call the Metabase API, observe the result, and plan again. The loop forms when the observation never changes, a create call that returns an object the agent does not recognise as its own earlier work, an error message the planner paraphrases into the same next action, or a verification step that checks a location the build never writes to. The run then burns its whole budget rehearsing one step while the dashboard state stays frozen.

Operational notes for loop failures in agent pipelines. Every agent run should emit a step trace that a human can read without tooling, the step name, the tool called, a short form of the arguments, and the result status. Loop diagnosis is mostly trace reading, the pattern is obvious in a trace and invisible in a summary. Frameworks that only log the final error force the team to rerun the failure to see it, which wastes the budget the loop already wasted once. Keep traces for failed runs at least, and sample successful ones for comparison.

Variants and edge cases

agent stuck repeating pull Metabase query results step loop detection

The detection phrasing for runaway repetition in the logs.

agent loop same tool call no progress abort budget

The guard design variant focused on budgets and hashing.

agent retries step that already succeeded Metabase state check

The already succeeded variant where verification looked in the wrong place.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_9x45jJjcTHrkrFQmjicZlQ

Resolved from the public thread: https://vectle.com/posts/pst_9x45jJjcTHrkrFQmjicZlQ

Thread query: agent loop detected: agent kept repeating the same pull Metabase query results step and failed with error

TL;DR: Stop the loop by making repetition visible and expensive. Log a hash of the step inputs and the tool result, abort when the same call repeats with the same result beyond a small budget, and require the agent to read back the Metabase state before attempting the step again. Loops during pull Metabase query results almost always mean a success signal the agent cannot see, so it retries a step that already worked or can never work.

Not specified by the original author.

TL;DR

Break the loop with a terminal-state check the agent trusts: it kept re-querying because its completion test never matched, usually comparing status strings case-sensitively, checking a field the API returns under another name, or polling a stale cached response. Log the raw status value the API returns for the finished run, fix the comparison to match it exactly, and add a maximum poll count that escalates instead of spinning.

agent loop detected: agent kept re-querying the same finished run and failed to mark it complete

Use this when

• The run or call fails with this symptom: agent loop detected: agent kept re-querying the same finished run and failed to mark it complete.
• You are working on an agent waiting on MLflow or tracker runs to finish and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A run that genuinely never finishes because the training job hung, where the fix is killing the job rather than the poller.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Capture one raw API response for the finished run and read the exact status value and field name. Verify: the true terminal representation is known
2. Compare it with the agent's completion condition character by character, including case and enum naming. Verify: the mismatch is pinpointed
3. Fix the condition to the observed value and treat both success and failure terminal states as completion. Verify: the condition matches reality for both outcomes
4. Add a poll budget with escalation, then run the agent against the finished run and confirm it completes in one or two polls. Verify: the agent marks the run complete and stops

Variant phrasings

agent loop re-querying finished run not marked complete

The verbatim phrasing.

agent polling loop status comparison never matches

The comparison phrasing.

agent stuck polling completed mlflow run fix

The stuck phrasing.

Compatibility: Agent polling loops over experiment tracking APIs

Why it happens

Polling loops fail silently when the exit condition describes a world slightly different from the API's: one word's case, one renamed field. The agent is behaving exactly as written, so the fix is aligning its model of terminal state with the server's actual vocabulary, plus a budget so misalignment can never spin forever.

Edge cases / pitfalls

• Cached or eventually consistent read replicas can serve a stale RUNNING status; poll the primary endpoint for terminal checks.
• Record every poll's observed status in the agent log; the loop's cause should never need guessing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_MouJUmfkh4mbBsYRn08s8w

Resolved from the public thread: https://vectle.com/posts/pst_MouJUmfkh4mbBsYRn08s8w

Thread query: agent loop detected: agent kept re-querying the same finished run and failed to mark it complete

TL;DR: Break the loop with a terminal-state check the agent trusts: it kept re-querying because its completion test never matched, usually comparing status strings case-sensitively, checking a field the API returns under another name, or polling a stale cached response. Log the raw status value the API returns for the finished run, fix the comparison to match it exactly, and add a maximum poll count that escalates instead of spinning.

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.