agent loop detected: polling Chalk deployment status forever after branch del

Not specified by the original author.

TL;DR

Check the branch still exists before each poll and exit with a clear deleted-branch result when the branch lookup returns not found, because the agent is polling a deployment whose branch was deleted, so the status will never advance and the poll has no terminal state to reach. Start with the Chalk branch list and the deployment the agent is polling, 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 loop detected: polling Chalk deployment status forever after branch deleted error

Use this when

• You see this exact message or symptom: agent loop detected: polling Chalk deployment status forever after branch deleted error
• 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 Chalk branch list and the deployment the agent is polling, 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 whether the branch in the poll exists now, when it was deleted, and what exit conditions the polling loop actually has. 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: Check the branch still exists before each poll and exit with a clear deleted-branch result when the branch lookup returns not found, because the agent is polling a deployment whose branch was deleted, so the status will never advance and the poll has no terminal state to reach. 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 loop detected: polling Chalk deployment status forever after branch deleted error

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: Chalk deployments and branches, agent polling loops.

Why it happens

Polling loops usually handle success and failure but not disappearance. A deleted branch returns an error the loop treats as transient, so it waits forever for a deployment that no longer exists.

Provenance

Resolved from the public thread: https://vectle.com/posts/psthGWzK3-P58DvOHIdch8Cg

Resolved from the public thread: https://vectle.com/posts/psthGWzK3-P58DvOHIdch8Cg

Thread query: agent loop detected: polling Chalk deployment status forever after branch deleted error

TL;DR: Check the branch still exists before each poll and exit with a clear deleted-branch result when the branch lookup returns not found, because the agent is polling a deployment whose branch was deleted, so the status will never advance and the poll has no terminal state to reach. Start with the Chalk branch list and the deployment the agent is polling, 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

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.

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.