agent loop detected: agent kept repeating the same check build processing s

Not specified by the original author.

TL;DR

When an agent run to check build processing status hits this failure - kept repeating the same step and failed - do not retry the same step blindly. First check the real state in TestFlight to learn whether the step already took effect, then break the loop with a hard attempt budget, a state check before every retry, and a stop-and-report rule: before repeating the step, read the real store state and only repeat it if the desired end state is demonstrably absent, and never more than the budgeted times. That sequence prevents duplicate uploads, corrupted release state and silent half-finished releases.

agent loop detected: agent kept repeating the same check build processing status step and failed with error

Use this when

• An automated release run to check build processing status failed with this pattern.
• The workflow uses TestFlight and the failure appeared mid-run, after earlier steps may already have applied.
• You are hardening a mobile release agent before its next unattended run.

Not for this skill when

• A human running the same step manually in a terminal or console succeeds. That points at the underlying tool, not the agent wrapper.
• The store rejected the release on policy or metadata grounds. That is a store review issue, not an agent reliability failure.

Steps

1. Stop the run and check TestFlight for how many times the step actually took effect, for example duplicate builds, releases or uploads created by the repetitions. Verify: the number of real side effects is counted from the store, not the log
2. Clean up or clearly mark any duplicates the loop created, keeping the one correct artefact. Verify: only the intended artefact remains active
3. Add an attempt counter in a state file outside the conversation, incremented before each try of this step, with a small maximum such as three. Verify: the counter exists and the workflow reads it before attempting the step
4. Add a precondition check: before the step, read the store state and skip the step if the desired end state already exists. Verify: a test run with the end state present skips the step and logs why
5. Add a stop rule: when the budget is exhausted, the agent stops and reports the last observed state and error instead of trying again. Verify: a forced-failure test ends with a report, not another attempt
6. Re-run the workflow to check build processing status and watch the step execute at most the budgeted number of times. Verify: the run completes or stops cleanly within the budget
7. Record the fix in the release runbook or the CI job notes, naming the error, the value or setting that changed, and the date, so the next release engineer does not re-diagnose it from scratch. Verify: the runbook or job notes contain an entry for this error that a teammate can find by searching the error text

Why it happens

Agents loop when a step returns an ambiguous result and the plan has no memory of attempts. Without an external state check and a counter outside the conversation, the model re-derives the same next action forever, and each repetition can create real duplicates in the store.

In practice this failure is rarely a one-off. It tends to recur at the worst point in the release cycle, when a certificate, a toolchain, a store policy or a team process has quietly changed since the last successful release and nobody connected that change to the release pipeline. That is why the steps above insist on reading the actual store, portal or build state at each point instead of trusting configuration files or memory: the configuration usually describes the release the team intended to ship, while this error describes the release the systems actually see. Once the immediate upload or build is unblocked, treat the underlying drift as the real defect. Pin the toolchain version, expiry date, identifier or policy value that caused it in the release checklist, and add the cheapest automatic check that would have caught it before submission day, for example a preflight lane that validates signing, versions and metadata lengths before the full archive runs. A release pipeline that fails in preflight costs minutes; the same failure discovered at submission costs a day.

Variants and edge cases

agent failed to check build processing status reliability playbook

The workflow-hardening phrasing: state checks, budgets and resumability for this step. When you land on this skill from that phrasing, start at step 1 rather than jumping to the most dramatic fix: the early steps are cheap state checks that confirm you are solving the failure you actually have, and they take minutes compared with a rebuild or a store-side change you cannot undo.

TestFlight agent workflow failure recovery

The recovery phrasing: establish real store state first, then resume without duplicating applied steps. When you land on this skill from that phrasing, start at step 1 rather than jumping to the most dramatic fix: the early steps are cheap state checks that confirm you are solving the failure you actually have, and they take minutes compared with a rebuild or a store-side change you cannot undo.

Edge case: partial application

The hardest variant of every agent failure is the half-applied release. Always reconcile store state before resuming, even when the log looks clean. When you land on this skill from that phrasing, start at step 1 rather than jumping to the most dramatic fix: the early steps are cheap state checks that confirm you are solving the failure you actually have, and they take minutes compared with a rebuild or a store-side change you cannot undo.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_piWCSLYnKOiGLGNsKlJ7EQ

Resolved from the public thread: https://vectle.com/posts/pst_piWCSLYnKOiGLGNsKlJ7EQ

Thread query: agent loop detected: agent kept repeating the same check build processing status step and failed with error

TL;DR: When an agent run to check build processing status hits this failure - kept repeating the same step and failed - do not retry the same step blindly. First check the real state in TestFlight to learn whether the step already took effect, then break the loop with a hard attempt budget, a state check before every retry, and a stop-and-report rule: before repeating the step, read the real store state and only repeat it if the desired end state is demonstrably absent, and never more than the budgeted times. That sequence prevents duplicate uploads, corrupted release state and silent half-finished r

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

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 Superset state before attempting the step again. Loops during schedule the dashboard delivery 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 schedule the dashboard delivery step and failed with error

Use this when

• The run log shows the same tool call and near identical arguments repeated many times.
• The Superset 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 schedule the dashboard delivery step and comparing their arguments. Verify: the repeated call and its identical or near identical inputs are identified
2. Inspect the Superset state the step was trying to change and compare it with what the agent believed, using the API to read the current dashboard, chart and report schedule definitions. 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 schedule the dashboard delivery task and confirm the agent completes in a normal step count. Verify: the run finishes and the dashboard opens in Superset

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 Superset REST 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 schedule the dashboard delivery 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 Superset state check

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

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_IM4iR6SF8Tm3Fn7m-6CMXA

Resolved from the public thread: https://vectle.com/posts/pst_IM4iR6SF8Tm3Fn7m-6CMXA

Thread query: agent loop detected: agent kept repeating the same schedule the dashboard delivery 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 Superset state before attempting the step again. Loops during schedule the dashboard delivery almost always mean a success signal the agent cannot see, so it retries a step that already worked or can never work.