What should you measure on an AI agent besides accuracy?
Accuracy tells you whether the final answer was right, but it hides how the agent got there. The metrics that actually predict reliability watch the process: did it pick the right tool, call it correctly, and recover when something failed; how many steps and how much it cost to finish; whether it stayed on the user's intent across a long conversation; and how often it needed a human to step in. An agent can be accurate in a demo and unreliable in production because none of those were measured.
AI Evaluation & ReliabilityAI Observability
Why accuracy alone misleads
A single accuracy score collapses a multi-step process into one number. It rewards an agent that stumbled into the right answer and punishes one that reasoned well but hit a broken tool. For anything that takes actions rather than producing one response, you need to measure the path, not just the destination.
Process metrics: how it got there
Watch the steps. Did the agent choose the correct tool, and call it with the right parameters? Did it perform the steps in a sensible order? When a call failed, did it recover or spiral? These catch the failures that an end-result score steps right over.
Cost and efficiency
The same task can be done in three steps or thirty. Track steps per task, token and dollar cost, and latency. An agent that gets there but burns ten times the budget is a production problem even when its accuracy looks fine.
Experience and oversight
Over a real conversation, does the agent hold the user’s intent or drift off it? How often does it need a human to take over? Intervention rate and conversation quality are the metrics that tell you whether people can actually rely on the thing — which is the only definition of reliability that matters.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
