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

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