Evaluating AI Agents: Metrics That Matter Beyond Accuracy

Accuracy is the metric everyone reports because it is the easiest to compute. It is also the metric that most often misleads. An agent that is 92% accurate but takes 40 seconds and costs $0.12 per run is a different proposition from one that is 92% accurate in 4 seconds for $0.004. The second ships; the first does not.

The four metrics that decide shipping

• Task completion rate: did the agent finish the job, end to end, without human rescue?
• Cost per task: model calls, tool calls, and retries, summed.
• Latency distribution: 50th and 95th percentile, not the average.
• Escalation rate: how often the agent handed off — and whether that was correct.

Measure tool-call success separately

Agents fail at the tool boundary more often than at the reasoning boundary. Track how often each tool call succeeds, returns the expected shape, and is acted on correctly. A flaky internal API will tank an agent’s reputation even when the model is fine.

Build a regression suite before you need it

Capture 100–200 representative tasks with known-good outcomes and run the agent against them on every change. A regression suite turns “I think we broke something” into “we broke two cases, here they are.” It is the single highest-leverage investment in agent reliability.

Report all four metrics together and you will make better shipping decisions than any accuracy number alone allows.