AI Agents at Scale: Visibility Is Not Accountability

Schneider Electric’s AI at Scale podcast with LangChain’s Sam Crowder, published October 26, focuses on observability and evaluation as agents move beyond demos. The company’s episode notes describe tracing how agents use tools and evaluating whether they perform as intended.

My take: visibility is necessary, but it does not decide who may act. A trace can help explain what happened; teams still need an agreed owner, a permission boundary, and clear conditions for pausing or resuming a consequential workflow.

What evidence should a team require before an agent can act without case-by-case human approval?