Repeatability
High
The task runs 3–4 times per quarter with the same Typeform structure, field types, and output format. The pipeline — extract, normalize, deduplicate, de-identify, export — is structurally identical each cycle, making it highly automatable once built.
Ambiguity Tolerance
Medium
Deduplication and de-identification have crisp rules, but free-text normalization (e.g., mapping 'startup' vs. '10-50 people' to a bucket) requires a predefined taxonomy. Without that schema documented upfront, the agent must make judgment calls that could silently introduce inconsistency.
Data & Tool Availability
High
Typeform has a well-documented API for response export, and the output is structured JSON or CSV. Standard Python libraries (pandas, spaCy, or an LLM call for fuzzy normalization) cover the full pipeline with no exotic dependencies.
Error Cost
Medium
Miscategorized free-text entries or missed duplicates could skew downstream analysis, but the CSV is a pre-analysis artifact — a human analyst reviewing the output before use provides a natural checkpoint. Errors are detectable and correctable before they propagate.
Human Judgment Required
Low
Once the normalization buckets and de-identification rules are defined, the work is mechanical transformation with no taste, ethics, or relationship context required. Edge cases in free-text can be flagged for human review rather than blocking automation.