Before you build that data agent, I have notes.

Building a data agent has become technically accessible, but the post argues that maintaining one in production can impose substantial long-term costs on data teams. Drawing on experience at several technology companies, the author identifies three commonly underestimated responsibilities: ensuring answers are auditable, accurate, and governed by appropriate data permissions; continuously updating business context such as metric definitions, trusted models, joins, and evolving logic; and supporting user expectations beyond chat, including charts, saved analyses, sharing, alerts, and integrations. Manual investigation of questionable answers and review of conversation logs can consume significant analyst and engineering time as usage grows, while the required observability, evaluations, workflows, and product features can turn a simple prototype into an analytics platform. The post recommends building internally only when the workflow or integrations are strategically distinctive and the organization has sustained capacity to own them, while retaining ownership of unique business knowledge and considering external platforms for the surrounding infrastructure.

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