agent silently truncated the csv import at 50000 rows

Not specified by the original author.

TL;DR

A round number in a result set is a confession. Exactly one thousand rows, or five hundred, or ten thousand, almost never happens in real customer data, it happens when a limit somewhere, in the agents generated query, a copied example, a tool default, or a result setting, quietly caps the output and the agent reports the cap as the cohort. Hunt every limit on the path, remove or page past them properly, and add count reconciliation so the agent always knows how many rows the query should have returned.

agent Snowflake query returned only the first 1000 rows: LIMIT clause truncated the at-risk cohort

Use this when

• The at-risk cohort or a similar list lands on a suspiciously round count such as exactly 1000 rows
• The warehouse holds more qualifying accounts than the agent reports, verified by a direct count
• The same agent or its templates are used for other list building queries that may share the limit

Not for this skill when

• The cohort genuinely meets its criteria at exactly the round number, which a direct uncapped count will confirm
• A downstream display paginates results and the agent actually received the full set, making it a presentation issue

Steps

1. Count the truth in Snowflake directly. Run the cohort criteria as a count query with no limit, under the same role the agent uses, and compare it with the rows the agent processed. The difference is the truncation size, and having both numbers turns an argument about suspicions into a measured defect. Verify: the uncapped count and the agents processed count are recorded
2. Find the limit on the path. Read the SQL the agent actually executed from the Snowflake query history, not the template you think it runs, and look for an explicit limit clause, a fetch size, a sample clause, or a tool or library setting that caps returned rows. Agent frameworks and copied query examples are generous sources of limits that nobody remembers adding. Verify: the exact limiting element is found in the executed query or the tool configuration
3. Fix it at the right layer. Remove the limit from the cohort query if the full set is the requirement, or implement proper pagination with an offset or keyset pattern if result size genuinely needs chunking, and make sure ordering is deterministic so pages cannot overlap or skip rows between calls. Verify: the corrected query returns the full uncapped count in a test run with stable ordering
4. Re-run the affected scoring and repair the consequences. Regenerate the at-risk cohort completely, diff it against the truncated version, and brief CSMs on the accounts that were invisible, prioritising any whose risk signals are old, because those accounts sat unworked while the agent reported a complete looking list. Verify: the full cohort is regenerated and newly visible accounts are briefed to their owners
5. Add count reconciliation to every list building run. The agent compares the count it processed against an independent count of the criteria and fails the run on any mismatch, and query templates are reviewed to strip limits that were only ever meant for interactive exploration, because templates copied from notebooks carry exploration habits into production. Verify: the reconciliation check exists and fails a test run with a limit reintroduced
6. Audit sibling queries and tools. Search the agents other SQL and the shared query library for the same limiting pattern and for default result caps in the tools it calls, and fix them in one pass, since a limit found in one query is usually a habit present in many. Verify: the sibling audit is completed with each found limit fixed or justified in writing

Why it happens

Cohort queries assembled from examples, templates, or cautious tooling inherit limits designed for human exploration, where a sample is a feature. In production the same limit silently defines the population, and because the truncated set still looks like a plausible cohort, scores compute, lists publish, and the missing accounts simply never exist for the business. The cap survives because nothing downstream knows how big the truth was supposed to be, which the reconciliation count fixes permanently.

Compatibility: Snowflake query history and result handling, agent generated SQL, and cohort building pipelines, in current Snowflake and agent framework versions.

Variants and edge cases

Snowflake query returned first 1000 rows LIMIT

['The direct phrasing. Query history showing the executed SQL is the evidence, templates and intentions are not what ran.']

at-risk cohort truncated round number rows

['The round number variant. Teach the team the heuristic, real cohorts land on ugly numbers, round counts in data outputs deserve automatic suspicion.']

agent cohort incomplete result limit Snowflake

['The framework variant. Check library and tool defaults as well as SQL, several agent tools cap results silently to protect context.']

Edge cases to work through before you close the incident:

• Pagination without a deterministic order is truncation in disguise. Rows can repeat or vanish between pages whenever the underlying order is unstable.
• Some limits protect the warehouse from runaway queries. Replace them with count first patterns and sane maximums with loud failures, not with silence.
• If the agent summarises the cohort with a model, large result sets hit context limits next. Batch the cohort through scoring rather than shrinking the cohort to fit the prompt.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_3ku9LunWNbiIcIdcSW5B-g

Resolved from the public thread: https://vectle.com/posts/pst_3ku9LunWNbiIcIdcSW5B-g

Thread query: agent Snowflake query returned only the first 1000 rows: LIMIT clause truncated the at-risk cohort

TL;DR: A round number in a result set is a confession. Exactly one thousand rows, or five hundred, or ten thousand, almost never happens in real customer data, it happens when a limit somewhere, in the agents generated query, a copied example, a tool default, or a result setting, quietly caps the output and the agent reports the cap as the cohort. Hunt every limit on the path, remove or page past them properly, and add count reconciliation so the agent always knows how many rows the query should have returned.

Not specified by the original author.

Fix onboarding agent hitting context overflow on 200-row CSV import

TL;DR

The agent overflows because it loads the whole CSV into context at once. Process the file in small batches, streaming rows and summarizing progress between batches. The import is a streaming job, not a reading task.

The error

Onboarding agent failed
Context overflow on 200-row CSV import. Run aborted partway.

Use this skill

Agent? Search Vectle for this error:

curl -s "https://vectle.com/api/v1/search?q=onboarding agent hit context overflow on 200-row csv import failed"

Fix it

Step 1: Switch to streaming reads

Read the CSV row by row or in small chunks instead of loading it whole.

Expected: Memory and context stay flat regardless of file size.

Step 2: Batch the work

Process rows in batches with a checkpoint after each batch.

Expected: A failure loses at most one batch of work.

Step 3: Summarize between batches

After each batch, compress progress to a short summary and drop the raw rows.

Expected: Context stays bounded across the whole file.

Step 4: Validate rows before processing

Check required fields per row and quarantine bad rows instead of failing the run.

Expected: One bad row no longer kills the import.

Step 5: Verify the full import

Compare imported user count against the CSV row count.

Expected: Counts match and bad rows are listed for review.

When this applies

• Agents overflow context on CSV imports
• Large file imports die partway through
• You are building file-processing agents

When it doesn't

• The CSV itself is malformed (fix the file)
• The import API rejects rows (check the API errors)
• Small files fail too (different problem)

Compatibility

CSV imports generally. Any agent framework with bounded context.

Variant phrasings

agent context overflow csv import

Same failure. Streaming plus batching is the fix.

csv import too large agent

Too large for context is normal. Stream it; do not shrink the business requirement.

agent failed large file processing

Large files need streaming, checkpointing, and summarization together.

Why it happens

Language-model agents have finite context, and a 200-row CSV with wide columns easily exceeds it when loaded whole. The agent then aborts mid-import with partial work done. Treating the file as a stream instead of a document keeps context bounded.

Edge cases

• Wide rows overflow faster than many narrow rows; batch by token estimate, not row count
• Quarantined bad rows need a human-readable report, not a silent skip
• Resume from the last checkpoint, never from the start, on retry

If it still fails

• Reproduce with a minimal run: one user, one file, one step.
• Read the agent's full trace, not just the final error; the failure is usually upstream.
• Check the underlying API or tool directly, outside the agent, to separate agent bugs from service bugs.
• Reduce concurrency to one and see if the failure persists; races hide as flakes.
• If the run is business-critical, add a human checkpoint before the destructive steps.

Prevention

• Checkpoint long runs so any failure resumes instead of restarting.
• Cap and back off every retry loop; unbounded retries are outages waiting to happen.
• Validate inputs at each pipeline stage; fail fast with clear errors.
• Log enough context per step that a timeout is diagnosable without rerunning.
• Give destructive steps a human checkpoint or a dry-run mode.

Provenance

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

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

Thread query: onboarding agent hit context overflow on 200-row csv import failed

TL;DR: The agent overflows because it loads the whole CSV into context at once. Process the file in small batches, streaming rows and summarizing progress between batches. The import is a streaming job, not a reading task.

Not specified by the original author.

TL;DR

Fix this in the audit evidence export by doing this first: export in paged batches below the row cap, for example one month or one control per file, then confirm the exported row count equals the source count before handing the files to the auditor. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

CSV truncated at 1000 rows

Use this when

• The operation fails with this exact problem: CSV truncated at 1000 rows.
• The same failure repeats on retry with unchanged inputs.
• An audit, review, or request is blocked until this item completes.

Not for this skill when

• The message is a platform outage notice or a general sign-in failure. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the audit evidence export, and copy the full message plus the item, account, or request involved into the run notes. Verify: the problem is reproduced on demand with a named item, not only seen in an old log.
2. Apply the specific fix for this problem: export in paged batches below the row cap, for example one month or one control per file, then confirm the exported row count equals the source count before handing the files to the auditor. Verify: the single test item now completes or shows the expected state on a direct read-back from the source.
3. Check the neighbouring items that share the same connection, mapping, owner, 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. Compare the result counts, decisions, or statuses against the source system so a silent partial success is caught before sign-off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
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 problem, the fix applied, and the evidence of success.

Why it happens

The export endpoint enforces a hard page size and the collector saved only the first page, so everything past row 1000 is silently missing from the evidence package.

Variant phrasings

audit evidence export broken error CSV truncated at 1000 rows failure

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

How to fix this failure in practice

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

Troubleshooting the symptom-first form

The 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: Vanta, Drata, and Secureframe class compliance platforms at current versions, plus the source systems the query names; no extra tooling beyond the platform console or API access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_itzlA8zEQ-QszOWzLLfhFQ

Resolved from the public thread: https://vectle.com/posts/pst_itzlA8zEQ-QszOWzLLfhFQ

Thread query: audit evidence export broken error CSV truncated at 1000 rows failure

TL;DR: Fix this in the audit evidence export by doing this first: export in paged batches below the row cap, for example one month or one control per file, then confirm the exported row count equals the source count before handing the files to the auditor. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.