Repeatability
High
The structure is consistent: ingest files, normalize fields, deduplicate, classify, export. This is the same pipeline every quarter, making it highly automatable with a reusable script or agent workflow.
Ambiguity Tolerance
Medium
Output format and tag taxonomy are clearly defined, but deduplication criteria (what counts as the same customer across channels) and topic boundary cases (e.g., a pricing complaint that's also a feature request) require upfront rule-setting. Once those rules are specified, success is measurable.
Data & Tool Availability
High
All data is already exported to files — no live API access or permissions are needed. The agent just needs the files handed over, which is a clean, low-friction setup.
Error Cost
Low
The output is a CSV for internal analysis, not a customer-facing or irreversible action. Mislabeled sentiment or a missed duplicate is easy to catch in a human review pass and costs nothing to fix.
Human Judgment Required
Low
Sentiment and topic classification at this scale are well within LLM capability. Deduplication across channels requires a defined matching heuristic (email, name, or content similarity), which a human should specify once but doesn't need to execute manually.