Evaluating AI agents as team members means applying the same accountability standards you use for human employees — SLA tracking, quality metrics, cost efficiency, and escalation rates. This scorecard framework gives you a structured way to evaluate any AI agent or automation tool, benchmark it against industry ranges, and get a data-backed recommendation: scale it, maintain it, or replace it.
Evaluate AI agents on four dimensions: SLA metrics (response time, availability, throughput), quality metrics (accuracy, error rate, escalation rate), cost metrics (cost per task, monthly total), and risk factors (error exposure, compliance, dependency concentration).
Industry benchmark: Excellent < 2%, Good 2–5%, Needs Improvement 5–10%, Poor > 10%. Thresholds vary by function — financial agents should target < 0.5%, content agents may tolerate 5–8% with review.
Escalation rate = % of tasks requiring human intervention. Industry average: 8%. High escalation means the agent is operating near its competency limit — typically signals the human/AI boundary needs redesign. Benchmark: Excellent < 5%, Good 5–10%, Poor > 20%.
Target > 99.9% for business-critical agents (< 8.7 hrs downtime/year). Acceptable: > 99.5% (< 43.8 hrs/year). Customer-facing agents should target 99.95%+. Factor planned maintenance into your SLA.