How to Benchmark AI Agents for Real Work

Generic benchmarks rarely tell you whether an AI agent will help your team. Real evaluation should use tasks that look like your actual work.

Build a useful benchmark

• Collect representative tasks from real workflows.
• Define success criteria before testing.
• Include easy, normal, and difficult cases.
• Measure quality, time, cost, and review effort.
• Track failures by category.

Compare against a baseline

Benchmark the current process first. If an agent saves time but creates more review burden or risk, the net value may be lower than expected.

Good agent benchmarks are workflow-specific. They help teams decide where autonomy is useful and where human judgment still carries the work.