Repeatability
Medium
The structure is repeatable — gather labor market data, income data, competitor density, synthesize — but the specific cities, zip codes, and firm context change each time, requiring fresh data pulls and judgment about which sources to trust.
Ambiguity Tolerance
Medium
The deliverable format (1–2 page report with go/no-go) is reasonably crisp, but success criteria for the recommendation itself are undefined — the agent doesn't know the firm's risk tolerance, capital constraints, or strategic priorities, making it hard to know when the work is truly done.
Data & Tool Availability
Low
This task requires live job posting data (Indeed, LinkedIn, or BLS APIs), salary trend data (BLS OES, Glassdoor), census income data by zip code, and competitor counts (possibly via Google Maps or Yelp API) — none of which are guaranteed to be pre-connected, and some require paid access or scraping.
Error Cost
High
A flawed go/no-go recommendation could lead to a costly office opening or a missed market opportunity; errors in salary or competitor data could materially mislead a real business decision with significant capital at stake.
Human Judgment Required
High
The final recommendation must weigh firm-specific factors — existing client base, owner risk appetite, capital availability, brand positioning — that the agent has no access to; the synthesis of quantitative data into a strategic recommendation is genuinely a human judgment call.