The Agent Works. Does Anyone Trust It? A framework for organizational AI autonomy

AI Engineer · youtube · 2026-10-14

AI Engineer interview with Michele Grant on why a technically working agent still fails to earn organizational trust.

Pilot shows agent reports matching analysts, yet the steering committee must decide if it enters core processes; the real question is measurable value, accountable ownership and evidence for greater authority
Measure against baselines: reporting time, analyst capacity, operating cost, material-issue detection; faster reports only matter if they change decisions or release real capacity — a good demo is not a business case
Three lines of accountability: workflow operators, risk overseers, independent assurance
A "Human Works Standard" records what the agent may do, where human judgment is required and who intervenes on failure
Auditable infrastructure: decision logs, agent/dependency inventory, autonomy scorecard; financial math stays in deterministic code
Four autonomy stages: shadow operation → reviewed drafts → exception handling with sampling → exception-focused review with independent checks; missed issues matter as much as flagged ones, and a falling override rate can mean weaker review

Note: the acquisition-integration examples are illustrative, not disclosed deployment results.

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