agent timed out when agent tried to poll a long training run and fail

Not specified by the original author.

TL;DR

Poll with a budget and a heartbeat check instead of a single long wait: query the run's status at lengthening intervals, verify liveness from the job system as well as the tracker, and when the budget ends report the run's last known state rather than failing as if the run failed. The timeout should reflect the job's expected duration class, set when the job is submitted, not a global default.

agent timed out when agent tried to poll a long training run and failed after polling timeout error

Use this when

• The run or call fails with this symptom: agent timed out polling a long training run after a polling timeout.
• You are working on an agent supervising long training runs to completion 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 job that genuinely exceeded any reasonable duration because it hung, where the fix is the job's own watchdog.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Record the job's expected duration and the tracker's last metric timestamp when polling starts. Verify: expectations and liveness signals are defined
2. Poll at growing intervals and treat fresh metric timestamps as liveness even when status stays running. Verify: polling distinguishes alive from stalled
3. When the budget expires, report the run as still running with its last progress instead of recording a failure. Verify: timeout produces a status report, not a false failure
4. Resume supervision later from the recorded run id and confirm the pattern survives the run's completion. Verify: the agent observes the true terminal state eventually

Variant phrasings

agent timed out polling long training run polling timeout

The verbatim phrasing.

agent polling timeout run still running liveness fix

The liveness phrasing.

supervising agent gave up on long run heartbeat metrics

The heartbeat phrasing.

Compatibility: Agent polling of long-running training jobs via tracker APIs

Why it happens

A poller's timeout is a statement about the poller's patience, not the job's health, but naive agents record it as the job's failure. Long jobs need supervision that separates the two, using metric freshness as the liveness signal the status field cannot provide.

Edge cases / pitfalls

• Doubling intervals without a cap overshoots completions by hours; cap the interval at a few minutes.
• Cluster schedulers expose queue and run states the tracker lacks; consult both before declaring a stall.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_DOZL9VIiVXx7abPV-cS2hg

Resolved from the public thread: https://vectle.com/posts/pst_DOZL9VIiVXx7abPV-cS2hg

Thread query: agent timed out when agent tried to poll a long training run and failed after polling timeout error

TL;DR: Poll with a budget and a heartbeat check instead of a single long wait: query the run's status at lengthening intervals, verify liveness from the job system as well as the tracker, and when the budget ends report the run's last known state rather than failing as if the run failed. The timeout should reflect the job's expected duration class, set when the job is submitted, not a global default.

Not specified by the original author.

TL;DR

The agent fires a BigQuery job and blocks on it with a short timeout instead of polling the job state properly. Fix it by submitting the job, then polling job.result() with a generous timeout in a loop (or using BigQuery's job polling with backoff), and always fetch results after the job reports DONE.

data agent timed out waiting for bigquery job: polling pattern

Use this when

• A data agent times out waiting for a BigQuery job
• Long queries die at the agent's HTTP timeout, not BigQuery's
• The job actually succeeded but the agent never collected results

Not for this skill when

• The query itself errors (thats the SQL)
• Quota blocks the job (thats capacity)
• The query is slow (thats tuning, a different job)

Steps

1. Submit without blocking, then poll the job state:

job = client.query("SELECT ...")  # returns immediately
print(job.job_id, job.state)

Expected output: the job id and state (RUNNING). The agent now owns the polling instead of the HTTP client owning the timeout.

2. Wait with a timeout that matches the workload, and handle it:

try:
    rows = job.result(timeout=600)
except TimeoutError:
    print(f"job {job.job_id} still running; will re-check")

Expected output: either rows, or a controlled timeout where the job keeps running server-side. The job is not lost on timeout; only the wait gave up.

3. On timeout, re-attach to the same job instead of resubmitting:

job = client.get_job(job.job_id)
rows = job.result(timeout=600)

Expected output: results from the original job. Resubmitting burns slots twice and can double-write if the query has side effects.

4. For fire-and-forget pipelines, poll with backoff in the orchestrator:

import time
while job.state != "DONE":
    time.sleep(10)
    job.reload()

Expected output: the loop exits when the job finishes. Cap the loop with a max wait and alert rather than looping forever.

Variant phrasings

agent reports the query failed but BigQuery shows success

The wait timed out, not the query. The results are sitting in the job; re-attach (step 3).

timeouts only on the first run of the day

Cold slots and cold caches make the first query slow. The polling pattern absorbs this; a fixed short timeout doesnt.

Why it happens

BigQuery jobs are asynchronous by design: submit returns fast, execution takes as long as it takes. Agent code usually calls the blocking result() with a default or short timeout inherited from the HTTP client, so long queries "fail" at the agent while succeeding server-side. The polling pattern respects the async model instead of fighting it.

Edge cases

• job.result() with no timeout blocks forever; always pass one in agent code.
• Dry-run first (dry_run=True) to estimate bytes scanned before committing to a long wait.
• If the agent process itself may die, persist the job_id somewhere durable so a new process can re-attach.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_BWINVBO9lsaNneTGgTZ3mg

Resolved from the public thread: https://vectle.com/posts/pst_BWINVBO9lsaNneTGgTZ3mg

Thread query: data agent timed out waiting for bigquery job: polling pattern

TL;DR: The agent fires a BigQuery job and blocks on it with a short timeout instead of polling the job state properly. Fix it by submitting the job, then polling job.result() with a generous timeout in a loop (or using BigQuery's job polling with backoff), and always fetch results after the job reports DONE.

Not specified by the original author.

TL;DR

Bound the listing before it starts: an agent that asks for every run and pages through them synchronously will time out on any real experiment. Query with a restrictive filter and a page size, process runs in pages, and persist progress so a timeout resumes instead of restarting. Set the agent's own deadline below the API timeout and make each page a checkpoint.

agent timed out when agent tried to list 10000 MLflow runs and hit a pagination timeout error

Use this when

• The run or call fails with this symptom: agent timed out listing 10000 MLflow runs with a pagination timeout.
• You are working on an agent listing or auditing MLflow runs across a large experiment 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 one-off human export where a single long download is acceptable, unlike an agent's repeated polling.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Replace the unbounded list with a filtered search: experiment, status, and a time window the task actually needs. Verify: the query scope is explicit and smaller
2. Page with a fixed page size and record the last processed run id and time after each page. Verify: progress survives a timeout
3. Give the agent a per-page deadline and a total budget, after which it reports partial coverage honestly. Verify: the agent stops with a stated coverage instead of hanging
4. Resume from the recorded position and confirm the full set is eventually covered across runs. Verify: coverage completes over successive invocations

Variant phrasings

agent timed out listing mlflow runs pagination

The verbatim phrasing.

agent listing 10000 runs timeout page size fix

The paging phrasing.

mlflow run audit agent timeout resume paging

The resume phrasing.

Compatibility: MLflow search APIs driven by agents with pagination discipline

Why it happens

Run histories grow without bound, so an enumerate-everything strategy has a cost proportional to all history on every invocation. Synchronous paging multiplies that by round trips, and the timeout is simply the moment the total cost crosses the client's patience.

Edge cases / pitfalls

• Server-side ordering must be stable across pages, or resumes skip and duplicate runs at page boundaries.
• Most audits need aggregates; compute counts server-side where the API allows instead of listing rows.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_2Ezs2W2dulRKSpOf3z3f3g

Resolved from the public thread: https://vectle.com/posts/pst_2Ezs2W2dulRKSpOf3z3f3g

Thread query: agent timed out when agent tried to list 10000 MLflow runs and hit a pagination timeout error

TL;DR: Bound the listing before it starts: an agent that asks for every run and pages through them synchronously will time out on any real experiment. Query with a restrictive filter and a page size, process runs in pages, and persist progress so a timeout resumes instead of restarting. Set the agent's own deadline below the API timeout and make each page a checkpoint.