Why agentic AI pilots stall before production

Scaling agentic AI from pilots to production depends less on model capability than on preparing reliable, accessible data and helping employees adopt new ways of working, according to a discussion featuring Onebee co-founder Jimmy Astle and Redpanda CMO Melissa Czapiga. Many projects stall because organizational data lacks clear schemas, access controls, or discoverability, while employees have not yet developed trust or habits for collaborating with agents. Astle describes adoption as a gradual progression from asking agents questions and working interactively with them to building reusable agents and eventually delegating tasks with live-system access, a transition that can take a year or more. Unlike controlled demos, production agents must handle ambiguous requests, high volumes, imperfect data, and consequential actions, requiring narrow initial scopes, durability to recover from failures, and governance such as least-privilege access, audit logs, usage visibility, approvals, and rollback mechanisms. The piece also notes that organizations must weigh the engineering cost of building this infrastructure themselves against using external platforms, highlighting Redpanda’s Agentic Data Plane as one governance-focused option.

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