What is an AI Agent ROI?

Calculating agent ROI honestly requires counting costs that are easy to omit: not just the per-run inference cost, but the engineering time to build and maintain the integration, the ongoing oversight needed to catch errors, and any rework caused by agent mistakes. On the benefit side, credible ROI measurement compares against a real baseline, what the task actually cost before the agent existed, rather than an idealized manual process that never quite happened in practice. Because agentic systems are probabilistic, ROI calculations should also account for failure rate and its downstream cost, an agent that completes 90% of tasks correctly but requires expensive human cleanup on the other 10% may have worse net ROI than a slower but more reliable process. The most trustworthy ROI claims measure the same metric before and after under comparable real-world load, rather than asserting improvement because the system “should” be faster or cheaper.