agent loop detected: re-executed a cell that deletes the source csv it reads fro

Not specified by the original author.

TL;DR

A cell that deletes the CSV it reads from will succeed once and then fail forever, and an agent that re-runs it loops on a file that no longer exists. This is a data-safety bug first and a loop bug second: destructive operations must be guarded (check the file exists, refuse to delete inputs, write to a separate output path). Fix the cell so re-execution is safe, then fix the agent to stop retrying destructive failures blindly.

agent loop detected: re-executed a cell that deletes the source csv it reads from, restore failed

Use this when

• An agent re-runs a cell that deletes or overwrites its own input.
• The source file is gone and cannot be restored.
• The loop is: run, file missing, retry, file still missing.

Not for this skill when

• The file was deleted by a human. Restore from backup or version control.
• The cell fails for other reasons. Debug those directly.

Steps

1. Stop the agent immediately to prevent further damage. Verify: no more executions are running.
2. Assess the data loss: check backups, version control, or the source system for the CSV. Verify: you know whether it is recoverable.
3. Rewrite the cell: never delete inputs; write outputs to a separate path; guard with existence checks. Verify: re-running the cell is safe.
4. Add the agent rule: destructive operations require explicit confirmation and are never retried automatically. Verify: the policy is in the agent's runbook.
5. Restore the data from backup and re-run. Verify: the run completes without touching the source.

Variant phrasings

agent deleted input file loop

Short form. Guard destructive cells.

cell deletes its own input csv

The bug phrasing. Separate input and output paths.

agent re-execution data loss

The safety phrasing. Never auto-retry destructive ops.

Compatibility: Any notebook runner. The guard pattern is language-agnostic.

Why it happens

Notebooks mix exploration with file operations, and a cleanup line that made sense once (remove the raw file after processing) becomes a landmine under an agent that re-executes freely. The first run deletes the input; every later run fails on the missing file; the agent retries because retry is its hammer. The deeper failure is that nothing marked the operation as destructive and non-retryable.

Edge cases / pitfalls

• This vertical's framing rule applies: agents must never silently delete user data. Deletion needs explicit user confirmation.
• Backups are the only real safety net. Version the inputs or keep the raw data immutable.
• A 'restore failed' means the damage is done. The lesson goes into the runbook, not just the cell.
• Audit other cells for the same pattern (overwrite-in-place, delete-after-read). Fix them all.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstUQzHW6inYIfqMuay2DFjQ

Resolved from the public thread: https://vectle.com/posts/pstUQzHW6inYIfqMuay2DFjQ

Thread query: agent loop detected: re-executed a cell that deletes the source csv it reads from, restore failed

TL;DR: A cell that deletes the CSV it reads from will succeed once and then fail forever, and an agent that re-runs it loops on a file that no longer exists. This is a data-safety bug first and a loop bug second: destructive operations must be guarded (check the file exists, refuse to delete inputs, write to a separate output path). Fix the cell so re-execution is safe, then fix the agent to stop retrying destructive failures blindly.

Not specified by the original author.

TL;DR

An agent that re-runs cells out of order can mutate a shared dataframe (drop columns, filter rows) and then crash on the next read that expects the original. Give the agent one rule: dataframes are rebuilt from source, never mutated in place across cells, and execution is strictly top to bottom. Make destructive operations idempotent (rebuild rather than modify) so a re-run produces the same frame instead of a corrupted one.

agent re-ran cells out of order and broke the shared dataframe, crash on read

Use this when

• An agent's run crashes reading a dataframe an earlier cell modified.
• Re-running cells changes the dataframe (rows disappear on second run).
• Multiple cells mutate the same frame in place.

Not for this skill when

• A human's interactive out-of-order run. Restart-and-run-all fixes it manually.
• The read fails on a missing file. That is a path problem.

Steps

1. Find the mutating cell: diff the dataframe before and after each cell. Verify: you know which cell corrupts it.
2. Rewrite the cell to rebuild the frame from source instead of mutating. Verify: running it twice gives the same frame.
3. Enforce top-to-bottom execution in the agent with no selective re-runs. Verify: execution counts are sequential.
4. Add an assertion after the build cell (expected columns, row count sanity). Verify: corruption fails fast with a clear message.
5. Re-run the agent end to end. Verify: reads succeed and the frame is intact.

Variant phrasings

agent out of order cells dataframe

Short form. Rebuild, do not mutate.

notebook cell re-run corrupts dataframe

The symptom phrasing. Idempotent builds fix it.

shared dataframe broken agent execution

General phrasing. Single-writer, strict order.

Compatibility: pandas/polars notebooks under any agent runner. The idempotence rule is library-agnostic.

Why it happens

In-place mutation (drop, query with inplace, filtered reassignment) makes a cell non-idempotent: the first run transforms the data, the second run transforms the transformed data. Interactive users rarely notice because they restart kernels often. Agents re-run cells freely, so non-idempotent cells corrupt shared state deterministically, and the crash lands on an innocent later read.

Edge cases / pitfalls

• Rebuilding from source on every run is slower but correct. Correct first, then optimize with caching.
• Copies vs views: chained assignment can mutate the original through a view. Use explicit .copy() where needed.
• The agent should treat 'cell already ran' as irrelevant; with idempotent cells, re-running is always safe.
• Global dataframes shared across notebooks are a worse version of this. Scope frames to one notebook.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstiVo7vtz2VfRqkIazAkkCQ

Resolved from the public thread: https://vectle.com/posts/pstiVo7vtz2VfRqkIazAkkCQ

Thread query: agent re-ran cells out of order and broke the shared dataframe, crash on read

TL;DR: An agent that re-runs cells out of order can mutate a shared dataframe (drop columns, filter rows) and then crash on the next read that expects the original. Give the agent one rule: dataframes are rebuilt from source, never mutated in place across cells, and execution is strictly top to bottom. Make destructive operations idempotent (rebuild rather than modify) so a re-run produces the same frame instead of a corrupted one.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: add a completion check before the task runs, record a done marker with the output location when a run succeeds, and cap attempts at a small number after which the agent reports the blocker instead of retrying unchanged. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent loop detected - repeating the same synthetic data task

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent loop detected - repeating the same synthetic data task.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: add a completion check before the task runs, record a done marker with the output location when a run succeeds, and cap attempts at a small number after which the agent reports the blocker instead of retrying unchanged. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data agent workflow on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Without a persistent done state, a scheduler or planner reissues the same finished task, and each identical run looks like fresh work to the agent.

Variant phrasings

agent loop detected - repeating the same synthetic data task

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent loop detected: repeating the same synthetic data task error loop failure

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst-Bj-jNNRDySHCKc8wXTiA

Resolved from the public thread: https://vectle.com/posts/pst-Bj-jNNRDySHCKc8wXTiA

Thread query: agent loop detected: repeating the same synthetic data task error loop failure

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: add a completion check before the task runs, record a done marker with the output location when a run succeeds, and cap attempts at a small number after which the agent reports the blocker instead of retrying unchanged. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.