Route by risk and confidence
Human review should trigger only when confidence is low or the action carries significant operational risk. This keeps throughput high while protecting quality.
Make reviews actionable in context
Reviewers need full context in one place: source evidence, proposed action, policy checks, and impact preview. Fragmented review tools slow teams and increase error rates.
- One-click approve/reject with rationale capture
- Inline evidence and provenance for each recommendation
- Feedback loop that updates prompts and evaluation rules
Use review data to improve automation
Review outcomes are a goldmine for continuous improvement. Track rejection patterns by workflow type and convert them into stronger policies, better prompts, and refined escalation rules.
The end-state is adaptive autonomy: more decisions safely automated over time, with fewer manual interventions.