context overflow when agent tried to summarize guild runs for full sweep error failed

Not specified by the original author.

TL;DR

Summarise runs in groups, never as one prompt: a full sweep of Guild runs overflows the context, so aggregate run metrics with a script into a per-configuration table, and give the agent the table plus the few runs worth individual reading. Sweep summaries are statistics problems. The agent adds value interpreting the aggregated table; it cannot add value holding five hundred raw run records.

context overflow when agent tried to summarize guild runs for full sweep error failed

Use this when

• The check or run fails with this symptom: a context overflow when an agent tries to summarise Guild runs for a full sweep.
• You are working on an agent workflow summarising Guild AI sweep results and the failure repeats on the same data or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A small sweep whose runs fit in context comfortably, where aggregation is still tidy but not required.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Export the sweep runs to a file and count them. Verify: the run count and the fields per run are known
2. Aggregate by configuration with a script: best, mean, and count per configuration. Verify: a compact table replaces the raw runs
3. Select the top and the anomalous configurations for individual inspection. Verify: a short list of runs gets full detail reads
4. Build the summary from the table and the inspected runs. Verify: every configuration is represented with its numbers
5. Save the aggregated table with the summary. Verify: the next sweep diffs tables instead of re-reading runs

Variant phrasings

context overflow summarize guild runs full sweep failed

The shorter phrasing focused on the named failure.

agent summarise guild sweep context error

The symptom phrasing a searcher tries next.

guild sweep aggregation summary fix

The variant naming the failing stage or tool directly.

Compatibility: Guild AI run exports, agent summarisation workflows for sweeps

Why it happens

Sweeps exist to produce many runs, and summarisation prompts grow linearly with them. The overflow arrives at exactly the sweep sizes that matter, and truncation drops runs silently, biasing the summary toward whichever runs happened to sort first.

Edge cases / pitfalls

• Keep failed and crashed runs in the aggregate with their own status; dropping them flatters every configuration.
• State the aggregation rule in the summary so configuration comparisons are reproducible.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_4bpP-MCCRN5O70d3x6biDQ

Resolved from the public thread: https://vectle.com/posts/pst_4bpP-MCCRN5O70d3x6biDQ

Thread query: context overflow when agent tried to summarize guild runs for full sweep error failed

TL;DR: Summarise runs in groups, never as one prompt: a full sweep of Guild runs overflows the context, so aggregate run metrics with a script into a per-configuration table, and give the agent the table plus the few runs worth individual reading. Sweep summaries are statistics problems. The agent adds value interpreting the aggregated table; it cannot add value holding five hundred raw run records.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: summarise in chunks with a map and reduce pattern, summarise each chunk to a short note saved on disk, then summarise the notes, so no step ever holds all records at once. 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.

context overflow when agent tried to summarize synthetic data records

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: context overflow when agent tried to summarize synthetic data records.
• 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: summarise in chunks with a map and reduce pattern, summarise each chunk to a short note saved on disk, then summarise the notes, so no step ever holds all records at once. 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

Single pass summarisation loads every record into one context, which overflows long before the summary is written on any realistically sized dataset.

Variant phrasings

context overflow when agent tried to summarize synthetic data records

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 - context overflow when agent tried to summarize synthetic data records error failed

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_h5AiApbg7sm9kNZXuOALjw

Resolved from the public thread: https://vectle.com/posts/pst_h5AiApbg7sm9kNZXuOALjw

Thread query: context overflow when agent tried to summarize synthetic data records error failed

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: summarise in chunks with a map and reduce pattern, summarise each chunk to a short note saved on disk, then summarise the notes, so no step ever holds all records at once. 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.

Not specified by the original author.

TL;DR

Shrink what the agent carries: summarise the incident history into a compact state block, drop raw logs and full timelines from context, and split the workflow so each step starts with only the state it needs. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

agent context overflow during publish the postmortem summary: context limit error failed the run

Use this when

• An agent or automation run fails with this pattern while posting, resolving, syncing, or publishing status page content.
• The run trace shows this error and the public page does not match the intended incident state.
• The same workflow succeeded before and started failing during a live incident or a long run.
• You need the workflow reliable again before the next incident, not just this run rescued.

Not for this skill when

• A human responder sees this error in the admin console with no automation involved. Use the error-specific status page skill instead.
• The underlying product outage is still unresolved. This skill repairs the communication workflow, not the outage.
• You are evaluating agent frameworks in general. This skill only fixes the named failure pattern.

Steps

1. Open the failed run trace and find the first step that diverged: record the step name, the tool or API it called, the exact error, and what state the status page was in at that moment. Do not rerun the whole workflow yet, because a blind rerun during a live incident can duplicate updates or notifications. Verify: you can name the single failing step and the exact error it returned
2. Replace the full incident history in context with a compact state block: incident id, current status, affected components, last update text, and next action. Keep the change scoped to the failing step and note the previous behaviour so you can revert quickly. Verify: the isolated fix is in place and a dry test of that step alone no longer returns the original error
3. Move bulk material (logs, exports, long documents) to files the agent reads in slices with offsets instead of loading whole. Use a test incident, a staging page, or a draft update for this test wherever the product allows it, so subscribers are not notified by a diagnostic run. Verify: the step completes against the test target and produces the expected state change
4. Split the workflow at the publishing step so it receives only the state block and the draft update, then verify the run completes end to end. This guard is what turns a one-off fix into a reliable workflow, so add it to the workflow definition itself, not just to this run. Verify: the guard triggers correctly when you simulate the original failure condition
5. Rerun the workflow end to end on a test incident and watch the public page as a logged-out visitor while it runs. Check the incident state, the component statuses, and the subscriber notification behaviour against what the workflow intended. Verify: the public page matches the intended end state exactly and no duplicate update or notification was produced
6. Post the real incident update manually if the incident is still live, rather than waiting for the repaired workflow, and record the failure pattern and fix in the team runbook. Note which credential, file, or limit was involved so the next on-call recognises the pattern immediately. Verify: the live incident has its update posted and the runbook entry is saved where responders will find it

Why it happens

Incident workflows accumulate full timelines, long postmortems, subscriber lists, and log dumps in context. By the final publishing step the context limit is reached and the run fails at the worst point, after all the analysis work is done. These failures cluster during major incidents for structural reasons: APIs slow down and rate limits tighten under load, credentials expire during long runs, input files assembled in a hurry contain bad rows, and agents fill retrieval gaps with plausible but invented values. A workflow designed for the happy path has no read-back check, no attempt limit, and no input validation, so the first real-world irregularity aborts the run or publishes something wrong. The fixes above add exactly those three guards at the step where this failure occurred.

Variants and edge cases

Same failure pattern against a different status or incident API

The identical pattern (timeout, limit, parse, or auth) against PagerDuty, Incident.io, FireHydrant, Better Stack, or Opsgenie instead of Statuspage. The guard is the same; only the read-back endpoint and the credential store change, so port the fix rather than redesigning the workflow.

Failure only during major incidents, never in tests

Load-dependent variants pass every test and fail live. Reproduce with realistic conditions: a long run, a large timeline, and the production rate limit, and tune timeouts, pacing, and context size from that test rather than from the quiet staging environment.

Agent reports success but the page never changed

The most dangerous variant, where no error surfaces at all. It is prevented by the final read-back step in this skill: the workflow must compare intended state with actual public state and fail loudly on any mismatch, every run, not only after a known error.

Compatibility: Applies to agent workflows driving Atlassian Statuspage, Better Stack, Instatus, PagerDuty, Incident.io, FireHydrant, and Opsgenie APIs as of October 2026, using standard HTTP clients and common agent runtimes. Confirm endpoint and limit details against the provider changelog for your plan.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_dgxWyfVI-BNGbqP6PXtpZw

Resolved from the public thread: https://vectle.com/posts/pst_dgxWyfVI-BNGbqP6PXtpZw

Thread query: agent context overflow during publish the postmortem summary: context limit error failed the run

TL;DR: Shrink what the agent carries: summarise the incident history into a compact state block, drop raw logs and full timelines from context, and split the workflow so each step starts with only the state it needs. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.