agent crashed parsing oversized patch compliance report (crash)

TL;DR

The report is data, and the agent is trying to hold all of it in the two scarcest resources it has: process memory and model context. Stream the parse instead: read the report incrementally with a streaming parser, extract only the records the wave needs (per-host verdicts and failures), write those to the tracker, and summarize aggregates in code, passing the model only the summary and the exceptions. If the source system can emit per-segment reports or answer queries, prefer that over one giant export.

agent crashed parsing oversized patch compliance report (crash)

Use this when

• The agent process dies (out of memory, killed) or the run fails with context errors at the report step.
• Small environments reports parse fine; the full fleet report never succeeds.
• Memory graphs climb steadily through the parse until the kill.

Not for this skill when

• The report file is malformed and a streaming parser also fails, at a specific record. That record is the bug to report to the report owner.
• The crash is in a different step that merely runs after parsing. Profile before redesigning.

Steps

1. Reproduce with the real report and observe memory growth and the failure point. Verify: the crash is tied to whole-file loading, with size numbers recorded
2. Rewrite ingestion as streaming: iterate records with a streaming parser for the format, process and discard each record, keeping only aggregates and flagged exceptions. Verify: memory stays roughly flat on the same file
3. Move summarization into code: counts by state, top failure classes, and the exception list are computed programmatically; the model receives those, not raw records. Verify: the context spent on the report step drops by an order of magnitude
4. Where the source allows, switch to segmented or query-based retrieval (per site, per collection) so no single response is huge. Verify: the largest segment processes comfortably
5. Add a size guard and a regression fixture: a synthetic oversized report in tests, and a runtime check that routes unexpectedly large inputs to the streaming path automatically. Verify: the fixture passes in CI and the guard triggers on a planted giant file

Variant phrasings

agent crashes parsing large compliance report out of memory streaming

The main phrasing.

streaming parser fleet report summarize in code not context

The design phrasing.

report too big segment query instead of full export

The source-side phrasing.

Compatibility: Compatibility: JSON, XML, and CSV reports from WSUS, ConfigMgr, and vulnerability scanners; streaming parsers exist in all common agent languages.

Why it happens

Report size scales with the fleet, so a load-everything design has a built-in fleet size at which it dies, and fleets only grow. The context angle makes it worse for LLM agents: even if memory survives, dumping raw records into the prompt burns the context budget the agent needs to reason, and the model drowns in rows it should never have read. Aggregation is a code job; judgment about exceptions is the model job, and the streaming pipeline is what keeps each in its place.

Edge cases / pitfalls

• Streaming parsers fail differently on malformed input (late, mid-stream); pair them with the completeness checks used for catalog data.
• Keep the exception records verbatim with their source offsets so a human can find them in the original report.
• If the report is regenerated on a schedule, process it on arrival in the background rather than at wave time, so the wave reads ready summaries.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_1OJoveROOE1li1uZ66r6bA

Resolved from the public thread: https://vectle.com/posts/pst_1OJoveROOE1li1uZ66r6bA

Thread query: agent crashed parsing oversized patch compliance report (crash)

TL;DR: The report is data, and the agent is trying to hold all of it in the two scarcest resources it has: process memory and model context. Stream the parse instead: read the report incrementally with a streaming parser, extract only the records the wave needs (per-host verdicts and failures), write those to the tracker, and summarize aggregates in code, passing the model only the summary and the exceptions. If the source system can emit per-segment reports or answer queries, prefer that over one giant export.