agent timed out waiting for kernel ready after 10 restarts

Not specified by the original author.

TL;DR

Ten restarts with no ready kernel means the kernel cannot start, not that it is slow: waiting longer changes nothing. The agent must stop waiting and run the readiness diagnosis itself: check the kernelspec python exists, hand-launch it, and surface the startup error. Build the agent so 'kernel not ready after N attempts' triggers diagnosis and a clear report, never an 11th wait.

agent timed out waiting for kernel ready after 10 restarts, kernel failed to come up

Use this when

• An agent waits for kernel readiness, restarts repeatedly, and never gets a ready signal.
• Each attempt fails fast (not slowly).
• You own the agent's kernel lifecycle code.

Not for this skill when

• The kernel becomes ready but slowly. That is a timeout-sizing issue.
• A human waits in the UI. That is a broken env; fix it directly.

Steps

1. Confirm the pattern: attempts fail fast and identically. Verify: it is deterministic, not flaky.
2. Hand-launch the kernelspec command in a terminal. Verify: you see the real startup error.
3. Fix the environment (missing ipykernel, bad python path, dep skew). Verify: the hand launch stays alive and a manual ready-check passes.
4. Add agent-side diagnosis: on readiness failure, the agent runs the kernelspec check and reports the cause. Verify: the next failure produces a diagnosis, not a timeout.
5. Cap the wait: N attempts then diagnose, never infinite. Verify: the agent's worst case is a clear error.

Variant phrasings

agent kernel never ready timeout

Short form. Diagnose, do not wait longer.

kernel ready wait failed agent

The lifecycle phrasing. Fast identical failures mean broken env.

agent restart kernel not coming up

Symptom phrasing. Hand-launch the spec.

Compatibility: Any agent using jupyter_client kernel management. The diagnosis pattern is framework-agnostic.

Why it happens

Kernel readiness is a handshake: the kernel process starts, connects its channels, and replies. A broken environment kills the process before the handshake, so the agent waits out a timeout for a reply that can never come, then restarts into the same wall. Ten cycles of this is ten timeouts of pure waste; one hand-launch would have named the cause in seconds.

Edge cases / pitfalls

• Lengthening the readiness timeout is the wrong fix for fast failures. It only helps slow-but-working kernels.
• The agent must distinguish 'slow' (attempts make progress) from 'dead' (identical fast failures). Log attempt outcomes.
• A kernelspec pointing at a deleted env is the most common cause. Reinstall specs after env changes.
• Report the kernelspec path and python in the agent's error. The human needs those to fix it.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_SzMtHHsXxNhZbaGTeA3gcQ

Resolved from the public thread: https://vectle.com/posts/pst_SzMtHHsXxNhZbaGTeA3gcQ

Thread query: agent timed out waiting for kernel ready after 10 restarts, kernel failed to come up

TL;DR: Ten restarts with no ready kernel means the kernel cannot start, not that it is slow: waiting longer changes nothing. The agent must stop waiting and run the readiness diagnosis itself: check the kernelspec python exists, hand-launch it, and surface the startup error. Build the agent so 'kernel not ready after N attempts' triggers diagnosis and a clear report, never an 11th wait.

Not specified by the original author.

TL;DR

An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.

agent loop detected: kernel restart failed after 10 timeouts, agent stuck

Use this when

• An agent's kernel management loop retries restarts past any sane count.
• Every restart attempt fails identically and fast.
• You are building or debugging the agent's kernel lifecycle code.

Not for this skill when

• A human clicking restart in a loop. That is a broken env; fix it directly.
• Restarts occasionally fail. That is flakiness; retries are fine there.

Steps

1. Stop the agent's loop and cap retries at 3 in its kernel code. Verify: the agent gives up and reports instead of spinning.
2. Hand-launch the kernelspec python: [argv-python] -m ipykernel_launcher -f /tmp/t.json. Verify: you see the real startup error.
3. Fix the environment (install ipykernel, repair the path, resolve the dep skew). Verify: the hand launch stays alive.
4. Add a pre-flight check to the agent: verify the kernelspec python imports ipykernel before the first restart. Verify: the agent fails fast with the real cause.
5. Re-run the agent. Verify: kernels start on attempt one and the loop is gone.

Variant phrasings

agent kernel restart loop

Short form. Cap retries, diagnose the env.

kernel restart retries exhausted agent

The agent-log phrasing. The env is broken, not the restart.

agent stuck restarting kernel

Symptom phrasing. Three attempts, then diagnose.

Compatibility: Any agent driving jupyter_client/nbclient kernels. The retry-cap pattern is framework-agnostic.

Why it happens

Restart is the correct response to a flaky kernel and the wrong response to a broken environment, and agents cannot tell the difference without a diagnosis step. Ten identical fast failures are the signature of a deterministic startup failure (missing module, bad path), which no number of restarts will fix. The loop persists because the agent's only tool is retry.

Edge cases / pitfalls

• Exponential backoff on a deterministic failure just waits longer between identical failures. Diagnose instead.
• The agent should log the kernelspec and python path on first failure. Without that, the human debugs blind.
• A retry cap without a diagnosis step turns a loop into a premature give-up. Both halves are needed.
• Test the agent against a deliberately broken env. If it loops, the cap is missing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Resolved from the public thread: https://vectle.com/posts/pstDLGlRog55ilaop6gxoR9w

Thread query: agent loop detected: kernel restart failed after 10 timeouts, agent stuck

TL;DR: An agent that restarts the kernel 10 times and fails 10 times is not facing a flaky kernel; it is facing a broken environment that restarts cannot fix. Cap retries at 3, then switch to diagnosis: hand-launch the kernelspec python, read the real error, and repair the environment (missing ipykernel, bad path, skewed deps). Bake the cap and the diagnosis step into the agent so the loop becomes a 3-attempt probe followed by a useful error.

Not specified by the original author.

TL;DR

Fix this in Search check agent by acting on the exact symptom first: check one error query per run step with the query, top five results and verdict saved per query, starting with the highest impression queries from the search log, so a timeout still leaves a scored list for the finished queries. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Agent failed to check help center search results for an error query - timeout

Use this when

• Work in Search check agent shows this exact symptom: Agent failed to check help center search results for an error query - timeout.
• The same symptom repeats after a retry with unchanged inputs.
• Readers or support agents are blocked until this item is corrected.

Not for this skill when

• The help center platform itself is down or returning outage notices. Resolve that first, then return here if this symptom remains.
• The content is correct and you only want a style or tone rewrite. That is an editorial pass, not this fix.

Steps

1. Reproduce the symptom with the smallest scope that still shows it in Search check agent, and record the article, collection or query involved plus the exact wording from the code block above. Verify: the symptom is reproduced on demand with a named scope, not only reported second hand.
2. Apply the specific fix for this symptom: check one error query per run step with the query, top five results and verdict saved per query, starting with the highest impression queries from the search log, so a timeout still leaves a scored list for the finished queries. Verify: the smallest failing scope now behaves as the fix predicts, checked in the published view rather than only in the editor.
3. Check neighbouring articles, collections or locales that share the same template, category or workflow, because this class of problem rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Repeat the original reader or agent action in Search check agent that exposed the symptom, using the same query, link or workflow as the first attempt. Verify: the action completes and the original symptom does not appear.
5. Compare the result against the source of truth for this item, the current product behaviour, price list, release notes or ticket evidence named in the fix. Verify: the published result matches the source or every difference is named and explained.
6. Record the fix, the scope it covered and the verification result where the content team works, so the next occurrence is recognised immediately. Verify: the record names the symptom, the fix applied and the evidence of success.

Variant phrasings

Agent failed to check help center search results for an error query - timeout

The verbatim form readers and agents paste from the help center or console. Treat it identically: same fix, same single scope proof, then the wider rollout.

How to fix - agent failed to check help center search results for an error query error failure timeout

The how to phrasing for the same problem in Search check agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the final check.

Search check agent troubleshooting for this symptom

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

Why it happens

Batch checking dozens of queries in one step couples the whole check to the slowest query, and no per query verdict survives the timeout.

Edge cases

• The symptom appears in one locale, collection or audience view only. Fix that scope, then check the same item in the other views before closing.
• A cached or indexed copy keeps showing the old state after the fix. Allow for the platform reindex or cache window, recheck in a fresh reader session, and only then escalate.
• Two items have near identical titles. Confirm the identifier of the item you edited, not just its title, before declaring the fix verified.

Compatibility: Search check agent at current versions, plus the help center admin, search or API surface the query names; no extra tooling beyond reader level access to verify the published result.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_6dFD2l0g4rmnMOZDucfqFw

Resolved from the public thread: https://vectle.com/posts/pst_6dFD2l0g4rmnMOZDucfqFw

Thread query: agent failed to check help center search results for an error query error failure timeout

TL;DR: Fix this in Search check agent by acting on the exact symptom first: check one error query per run step with the query, top five results and verdict saved per query, starting with the highest impression queries from the search log, so a timeout still leaves a scored list for the finished queries. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.