## TL;DR
Two hundred full transactions do not fit in a context window, so the agent truncates silently and reviews whatever survived. Stop feeding raw transactions: the agent summarizes each transaction to a fixed-size record, reviews in batches of twenty, and keeps a running risk ledger across batches. The full detail stays one tool call away whenever the agent needs to drill down further.

```text
fraud agent context window overflow during 200-transaction review
```

## Use this when
- Reviews of large batches miss obvious patterns at the tail.
- The agent errors on context length or silently drops input.
- Batch review quality falls as batch size grows.

## Not for this skill when
- The agent fails on small batches too. That is a prompt or tool problem.
- You need real-time per-transaction decisions. That is a streaming scorer, not a batch review.

## Steps
1. Compress each transaction to a fixed schema: amount, signals, entities, score. Verify: 200 compressed records fit with room to spare.
2. Review in batches of twenty with a running ledger: carry open hypotheses forward, never restart. Verify: a pattern spanning batches one and eight is still caught in the final review pass.
3. Let the agent pull full detail on demand: the ledger references transaction ids, and one tool call fetches the full record. Verify: drill-downs work from the summary.
4. Cap the batch size by measured tokens, not by count: stop adding records before the window fills. Verify: a token budget check runs before each batch and skips cleanly when the window is tight.

## Variant phrasings
### too many transactions for agent context
Capacity phrasing.
### batch review truncated silently
Truncation phrasing.
### agent lost context mid review
State phrasing.

Compatibility: Works with any LLM-based review agent. The summarize-batch-ledger pattern is model agnostic.

## Why it happens
The pipeline hands the agent a raw dump and trusts the framework to handle it. Frameworks truncate or error, and the agent reviews the survivors as if they were the whole batch.

## Edge cases / pitfalls
- Summaries can drop the one field that mattered. Let the agent request fields back.
- The ledger itself can grow. Compact it every few batches.
- Ordering matters for velocity patterns. Keep chronological order in the compression.
- Log the token count per batch, or you cannot tune the cap when the model changes.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_t0DwNIdsyB5BBan6fl6mFg