Enterprise AI Measurement Guide
Agent Effectiveness
Agent Effectiveness Score
How do we measure whether our engineers are using AI coding agents effectively, and where do we need to invest in training rather than better tools?
What it shows
The Agent Effectiveness Score is a composite score that measures how well engineers are using AI coding agents, not how capable the agents are. It evaluates seven behavioral dimensions: prompt clarity (how precisely engineers specify what they want), session steering (how actively they guide the agent mid-session), sophistication (how advanced the use cases they attempt are), prompt quality (the structural quality of their prompts), verification discipline (whether they verify agent output before accepting), task outcomes (whether sessions produce merged PRs), and user sentiment (how engineers rate the agent's output). The score distinguishes engineer proficiency from agent capability.
Why it matters
A 91/100 Agent Effectiveness Score alongside a 10% Outcome Success rate reveals the core insight: the constraint is often human behavior, not AI capability. Organizations investing in better coding agents when the real bottleneck is engineer proficiency with those agents are solving the wrong problem. The Agent Effectiveness Score gives CTOs and CHROs the data to make that distinction, and to invest in the right intervention (training and workflow design, not additional tool spend).
The Larridin angle
Most AI coding platforms measure agent performance. Larridin measures engineer performance with agents, a fundamentally different question that produces actionable interventions rather than vendor comparison data.