When an AI agent returns a query result, it faces a design decision: what chart type best represents this data? The wrong choice can mislead — a pie chart for 15 categories, a line chart for non-temporal data, a bar chart with no sorting. Getting this right is a UX challenge disguised as a technical one.
The Chart Selection Logic
The agent evaluates three factors: data shape (how many dimensions, how many measures, cardinality), analytical intent (comparison, distribution, trend, composition), and user context (are they on mobile, what did they ask before). Based on these, it applies a decision tree: time series → line chart, categorical comparison → bar chart, part-of-whole with ≤5 categories → pie or donut, correlation → scatter plot.
When to Override the Default
Rules aren't enough. If the user asks 'Show me the trend', a line chart is obvious. But if they ask 'Which region is perfor分钟g best?', a sorted bar chart communicates the ranking better than a table. The agent should understand the question's intent, not just the data's shape.
Mobile-First Visualization
On mobile (where most conversational BI interactions happen), chart selection is constrained by screen size. Pie charts with more than 4 slices are unreadable. Bar charts with more than 8 bars need horizontal scrolling. The agent must simplify — aggregate, filter, or select a different chart type that works on a 375px-wide screen.
The Human Override
Auto-generated charts should always be overridable. If the user says 'Show me this as a table' or 'Make it a scatter plot', the agent should comply instantly. The goal is to get the right chart 90% of the time automatically — and make manual selection effortless for the other 10%.
核心要点
- The Chart Selection Logic
- When to Override the Default
- Mobile-First Visualization
- The Human Override
总结
Auto-generated charts should always be overridable. If the user says 'Show me this as a table' or 'Make it a scatter plot', the agent should comply instantly. The goal is to get the right chart 90% of...
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