What matters most in an AI agent: model quality, reliability, cost, or workflow convenience?

For an AI agent, I would rank the criteria like this:

1. Reliable task completion. A fluent answer is not enough if the agent edits the wrong file, calls the wrong API, or silently fails.

2. Correct tool use and recovery. Can it validate results, recover from a timeout, and stop when the plan is no longer safe?

3. Cost and latency. These matter only after the first two are measured on the real workload.

4. Model quality in the abstract. General benchmark scores are useful, but they are a poor substitute for a task-specific evaluation.

AgentBench treats agents as systems acting in environments rather than as text generators. More recent work such as ReliabilityBench argues that single-run success rates miss consistency, robustness to changed wording, and tolerance of tool or API failures.

My practical score would be: successful end states over repeated runs, divided by total time and cost, with separate penalties for unsafe actions and human interventions. I would also test the same task several times and inject failures deliberately.

The best agent is therefore rarely the one with the highest benchmark score. It is the one that completes the target work predictably, exposes what it did, and fails in a way a human can notice and correct.