Repeatability
High
The same six fields must be extracted from every document, and the flagging logic is consistent. Mixed layouts add friction but don't change the underlying structure of the task.
Ambiguity Tolerance
High
Success criteria are crisp: populate six named fields per row, flag rows where address, sq ft, or rate couldn't be confidently extracted. There's no subjective judgment about what 'done' looks like.
Data & Tool Availability
High
The agent needs the 340 PDFs and an OCR-capable pipeline (e.g., AWS Textract, Azure Form Recognizer, or a Python stack with Tesseract + LLM parsing). All of these are readily available and well-suited to this input format.
Error Cost
Low
The output is a CSV for human review before database upload, so errors are catchable before they propagate. Flagging uncertain rows further reduces the chance of bad data entering the system silently.
Human Judgment Required
Low
Field identification is rule-based and the flagging logic is explicit. The only edge cases requiring human review are already surfaced by the flagging mechanism, so no ongoing human judgment is needed during extraction.