Repeatability
High
The transformation rules are consistent across all files: strip filler words, fix errors, standardize formatting, extract metadata, output CSV. Each call is structurally the same unit of work, making this highly automatable at scale.
Ambiguity Tolerance
Medium
Most success criteria are crisp (column names, filler word list, formatting), but 'obvious transcription errors' and 'seems incomplete or problematic' require judgment calls that vary by call. The QA note column is a smart escape valve for these edge cases.
Data & Tool Availability
High
The inputs are plain text files with timestamps — no special APIs or live data needed. The agent needs file access and a CSV writer, both trivially available. Metadata like call_date and agent_name must be parseable from the files or filenames, which is the one dependency to verify.
Error Cost
Low
Errors are reversible — the original auto-generated transcripts are preserved, and the output is a structured CSV that a human can audit. Misattributed speakers or missed errors are annoying but not catastrophic, especially with a QA note column flagging suspect calls.
Human Judgment Required
Low
The task is almost entirely mechanical: pattern matching, regex-style substitution, and format normalization. The only genuine judgment calls — ambiguous speaker turns, borderline incomplete calls — are explicitly handled by the QA note column rather than requiring a human decision mid-task.