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AI agents work continuously in the background — proposing merges, flagging anomalies, and surfacing data quality issues. This is where humans review and decide.

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Quality Metrics

Pricing completeness, forecast coverage, and open review count — the three pillars of data governance health. High percentages mean the data foundation is trustworthy; open reviews are the “to-do list” for maintaining quality. Keep open reviews low — each accepted suggestion improves the overall data quality score.

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Review Queue

AI-proposed actions waiting for human approval: material merges, pricing anomaly flags, and data quality improvements. This is the “agents as co-workers” model — the AI does the analysis and proposes actions; planners make the final call with full context and confidence scores. Review each suggestion: accept to apply, reject to dismiss. Every decision is logged.