AI does the groundwork. Experts safeguard the outcome.

The organization and its appointed human owners remain responsible for decisions and published outcomes. The agent supports work within agreed boundaries; it does not remove accountability. We define who reviews sensitive output, who may approve actions and how exceptions are escalated, so responsibility is clear before the workflow becomes operational.

Actions involving budgets, legal or commercial claims, personal data, publication and significant customer impact commonly require explicit review. The exact list depends on the workflow and risk. We establish approval rules during design and can require stronger controls for new or unstable tasks until output quality has been assessed over time.

Yes, for specific low-risk steps when evidence shows that output is stable and exceptions are handled correctly. Reduced review does not mean removing oversight entirely. Logging, thresholds and escalation should remain in place, and higher-impact decisions can continue to require approval even when routine preparation becomes more automated.

Specialists can review outputs against documented criteria, correct source information and refine instructions, tools or approval thresholds. Feedback should be captured in the workflow rather than supplied informally. This creates a traceable improvement process and helps distinguish a model limitation from missing data, unclear policy or an incorrectly designed task.

We design workflows to show the relevant source, status and review context wherever the connected tools allow it. That does not make every model calculation fully interpretable, but it can make operational decisions auditable. Important proposals should be accompanied by enough evidence for a reviewer to accept, reject or investigate them.