Accuracy is the metric every AI agent evaluation starts with, and the one that least reflects whether an agent is actually delivering business value. Enterprises that stop at accuracy miss the operational signals that predict whether an agent should keep running in production.
Track Task Completion, Not Just Correctness
An agent can be technically accurate on the sub-tasks it completes while still failing to finish the overall job a human needed done — getting stuck, escalating unnecessarily, or timing out. Completion rate on real end-to-end tasks is a more honest signal of usefulness than isolated accuracy scores.
Watch Escalation Patterns Closely
How often an agent hands off to a human, and why, reveals more about its real-world limits than any benchmark. A rising escalation rate on a specific task type is an early warning sign worth investigating before it becomes a larger failure.
Connect Agent Metrics to Business Outcomes
Ultimately, agent KPIs should roll up to something the business already tracks — resolution time, cost per case, conversion rate — not exist as a separate AI scorecard nobody outside the AI team reads. This is what makes agent performance data useful in budget and scaling conversations.
The enterprises getting the most value from AI agents are measuring how the agent performs in the messy real world, not just how it scores on a clean test set.