Repeatability
High
The transformation logic is structurally identical for every transcript: parse speaker labels, timestamps, and text, then map to a fixed JSON schema. Variation across Rev, Otter, and manual formats is finite and can be handled with format-specific parsing rules.
Ambiguity Tolerance
High
The target schema is explicitly defined with five named fields, and success is objectively measurable — every record either conforms to the schema or it doesn't. Confidence score handling for services that don't provide one (e.g., manual transcripts) is the only minor ambiguity, easily resolved with a null or default convention.
Data & Tool Availability
High
The agent needs only the raw transcript files and the schema definition, both of which the user already has. No external APIs, live data, or special permissions are required — this is a pure file-in, JSON-out transformation.
Error Cost
Low
Mistakes are easily caught: a human reviewer can spot-check a sample of output records against source transcripts before committing to thematic analysis. No irreversible actions are taken, and the source files remain untouched throughout.
Human Judgment Required
Low
The task is structural normalization, not interpretation — the agent doesn't need to understand the content, only parse and reformat it. The one judgment call is resolving ambiguous speaker labels (e.g., 'Speaker 1' vs. 'Interviewer'), which can be handled with a simple heuristic or flagged for human review.