The Effectiveness Scorecard: A clearer view of where to focus

Yesterday, we introduced Span’s AI Effectiveness suite, built on agent traces and agent evals.

The first experience we’re unveiling is the Effectiveness Scorecard: a leadership-level view of how AI effectiveness is trending across the organization, where gaps are emerging, and where to focus next.

It gives engineering leaders a clear baseline for understanding AI effectiveness and how it changes over time.

Built on agent traces and evals

The Effectiveness Scorecard is powered by agent traces: the interaction history between developers and AI tools, including prompts, iterations, tool calls, file edits, and more. Span analyzes those traces and applies agent evals across sessions to synthesize patterns across interaction history.

That gives leaders a more direct view of effectiveness than usage metrics alone. Instead of just seeing whether AI is being used, they can see how effectively teams are working with it.

What leaders see

At the center of the report are org-level scores across key dimensions—like user sentiment, agent satisfaction, prompt quality, and other signals that shape AI effectiveness in practice.

Beyond the scores, the report surfaces the context leaders need to understand why and what to do next:

Trends over time

See how effectiveness is changing across the organization and establish a measurable baseline for improvement.

Patterns by team or topic

Filter by team, theme, or area to understand where stronger or weaker patterns are emerging.

Top contributing traces

Drill down from a lower score or recurring issue into the traces that contributed most to it.

Common friction and blockers

Surface recurring signs of user frustration, workflow breakdowns, or environmental issues that are making agents less effective.

Supporting context

Bring in related signals like repository health and emerging patterns that help explain why effectiveness looks the way it does.

A practical starting point

Effectiveness scores give leaders a baseline. The value is in the context, patterns, and recommendations that show where to focus next. More soon on how teams can turn that understanding into continuous improvement.