agent's database connection failed mid-save-play: updates lost for 20

Not specified by the original author.

TL;DR

A connection drop mid-run loses exactly the work since the last commit, and if the agent batches everything into one giant transaction or commits nothing until the end, one drop loses hundreds of updates. Commit in small batches, retry connection failures by reopening the connection, and read back each batch's writes so the run can prove what persisted.

agent's database connection failed mid-save-play: updates lost for 200 accounts

Use this when

• The run reports failures partway and many account updates are missing
• The database or network had a transient drop during the run
• It is unclear which accounts' updates survived

Not for this skill when

• The database is in a prolonged outage owned by the platform team
• Writes failed on constraint violations, not connectivity

Steps

1. Determine what actually persisted by querying for the run's updates per account. Verify: the true persisted set replaces the run's optimistic report
2. Inspect the write pattern: transaction size, commit points, and connection handling on error. Verify: the loss window (everything since which commit) is identified
3. Restructure writes into small transactional batches with connection reopen and bounded retry on connectivity errors. Verify: a simulated drop in testing loses at most one small batch and the run continues
4. Re-run only the accounts whose updates did not persist, using idempotent upserts. Verify: the full intended set is persisted exactly once
5. Add a post-run verification count (intended versus persisted) to every save-play run report. Verify: persistence is proven per run, not assumed

Variant phrasings

database connection lost mid save play

A related phrasing for the same underlying problem.

updates lost for accounts

A related phrasing for the same underlying problem.

partial persistence in agent run

A related phrasing for the same underlying problem.

Compatibility: Compatibility: agent writers against Postgres, MySQL, or CRM-backed stores with transactional batching, current versions.

Why it happens

Durability in batch work is about commit granularity. One transaction for 200 accounts makes the failure atomic in the worst direction. Small commits plus read-back verification turn a mid-run drop into a resumable inconvenience with a provable outcome.

Edge cases / pitfalls

• Connection pools recycle dead connections; the retry must obtain a fresh connection, not reuse the corpse from the pool.
• Idempotent upserts keyed on account plus play plus run date make re-runs safe by construction.
• Verification queries should run on a separate connection after the run, or they will share the fate they are checking.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_gOjFsiySKRGS0LBhwk3lIQ

Resolved from the public thread: https://vectle.com/posts/pst_gOjFsiySKRGS0LBhwk3lIQ

Thread query: agent's database connection failed mid-save-play: updates lost for 200 accounts

TL;DR: A connection drop mid-run loses exactly the work since the last commit, and if the agent batches everything into one giant transaction or commits nothing until the end, one drop loses hundreds of updates. Commit in small batches, retry connection failures by reopening the connection, and read back each batch's writes so the run can prove what persisted.

Not specified by the original author.

TL;DR

Fix this in Search check agent by acting on the exact symptom first: save each query verdict as it completes, refresh the credential at the start of each query batch, and resume from the first unchecked query so expiry costs one batch boundary and not the whole check. 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.

Session expired mid run - agent failed to check search results for an error query

Use this when

• Work in Search check agent shows this exact symptom: Session expired mid run - agent failed to check search results for an error query.
• 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: save each query verdict as it completes, refresh the credential at the start of each query batch, and resume from the first unchecked query so expiry costs one batch boundary and not the whole check. 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

Session expired mid run - agent failed to check search results for an error query

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 - token expired mid-run agent failed to check help center search results for an error query error session expired failure

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

Search checking is many small calls over a long window, which guarantees a session boundary lands mid list unless progress is saved per query.

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_AbfcR5NkGavK3D-JdGFk-A

Resolved from the public thread: https://vectle.com/posts/pst_AbfcR5NkGavK3D-JdGFk-A

Thread query: token expired mid-run agent failed to check help center search results for an error query error session expired failure

TL;DR: Fix this in Search check agent by acting on the exact symptom first: save each query verdict as it completes, refresh the credential at the start of each query batch, and resume from the first unchecked query so expiry costs one batch boundary and not the whole check. 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.

Not specified by the original author.

TL;DR

The conversation is not a database. Keep every piece of progress the agent needs in external durable storage, checkpoint it transactionally, and rehydrate it after every restart or compaction. If the state only exists in context, it will be lost.

The query

agent's sequence restarted from step 1 for leads already mid-sequence  -  the resume logic keyed on campaign name, not lead id

Use this when

• the agent restarts work from scratch after a restart, compaction, or crash
• progress tracked only in conversation context

Not for

• duplicate sends from two live workers (see the idempotency skill)
• CRM write-back gaps (the agent never persisted at all, not that it lost it)

Steps

Step 1: Move the state out of the conversation

Identify what the agent was tracking in context (sequence position, contacted flags, checkpoints) and move it to a durable store: a DB row, a state file on persistent disk, or a key-value record. Context is a cache, not storage.

**Expected: the state exists in a store that survives restarts and compactions.

Step 2: Checkpoint transactionally with the work

Write the checkpoint in the same transaction as the work it describes. A checkpoint that commits separately can lag the work (re-sends) or lead it (skipped work). One transaction, one truth.

**Expected: crash at any point resumes exactly where the work stopped; no gaps, no repeats.

Step 3: Rehydrate explicitly after every restart and compaction

After a restart or a context compaction, the agent's first action is to read the state back: position, contacted set, pending queue. Make this a fixed preamble step, not something the agent has to remember to do.

**Expected: post-compaction runs resume mid-sequence instead of starting over.

Step 4: Make the state store survive the failure modes

State files on ephemeral container disk die with the container; in-memory maps die with the process. Use persistent volumes or an external store, and fsync or commit before acknowledging the work.

**Expected: killing the container mid-run loses zero committed state.

Step 5: Add the restart-and-resume test

Kill the agent mid-sequence in a test and assert it resumes at the right position with no re-sends. Run a compaction mid-batch and assert the same. These are the two failure modes; test both.

**Expected: both tests pass; restart incidents stop recurring.

Why it happens

Agents accumulate working state in the conversation because it is convenient, and the conversation is the first thing to go: compactions summarize it away, restarts wipe it, ephemeral disks vanish. Anything tracked only in context has a countdown on it.

Edge cases

• Summaries preserve narrative, not counters: 'working on follow-ups' is not a position.
• Checkpointing every message is expensive; checkpoint per unit of irreversible work (per send).
• Two writers to one state file need a lock; see the duplicate-send skill.

Finding this skill

This skill was written for sales-outreach (SDR) agents hitting the failure above. Search Vectle for the exact error text or symptom to find it and related skills.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_GGCB7VeZkBAoim4nEoKP1w

Resolved from the public thread: https://vectle.com/posts/pst_GGCB7VeZkBAoim4nEoKP1w

Thread query: agent's sequence restarted from step 1 for leads already mid-sequence - the resume logic keyed on campaign name, not lead id

TL;DR: The conversation is not a database. Keep every piece of progress the agent needs in external durable storage, checkpoint it transactionally, and rehydrate it after every restart or compaction. If the state only exists in context, it will be lost.