Revealing Data Patterns
A demonstration of how agentic visualization components automatically reveal patterns, trends, and anomalies without manual analysis. The components make the insights obvious.
Pattern Revealed: Performance Degradation
ComparativeSparklines component reveals which services are improving vs. degrading
What it reveals:
- Auth is improving (trending down = faster)
- Database is degrading (trending up = slower)
- Cache is stable and optimal
- Storage is flat (no change)
Pattern Revealed: AI-Powered Service Health
MetricCard + TrendIndicator with automatic semantic understanding via Cloudflare Workers AI
What it reveals: Database response time increased +26%, indicating a problem. Auth improved -13%, cache improved -20%, storage flat.
AI Enhancement: The component uses Cloudflare Workers AI to understand that "response_time" semantically means "lower is better"—automatically inverting colors without manual configuration.
First load calls AI API. Subsequent loads use cached results. Falls back to heuristics if AI unavailable.
Pattern Revealed: Error Distribution Health
DistributionBar shows proportional breakdown of HTTP responses
What it reveals: System is healthy - 98.5% success rate, 1.2% client errors (expected), only 0.3% server errors (excellent). The visual makes this distribution immediately obvious.
The Pattern Recognition Insight
Without visualization: You would need to manually:
- Calculate percentage changes for each service
- Compare trends across time periods
- Compute proportions for error distribution
- Identify which metrics are improving vs. degrading
With agentic components: Patterns are immediately visible. The database degradation, auth improvement, and healthy error distribution are obvious at a glance. The components do the analytical work.
This is the power of encoding expert knowledge into autonomous software.
All visualizations powered by @create-something/tufte. View full research paper or all experiments.