Evaluating AI Agents: From Tool Calls to Full Task Completion

Tool-call evaluation checks a single call (function name and arguments), while task-completion evaluation measures whether the agent executed a long chain of sequential calls in a live environment and reached the required outcome, including recovery from a failed step.

Because an answer can look correct linguistically and logically while the agent fails to do the actual work — such as not completing a booking, not updating a record, or stalling at the first error. Text quality does not reflect execution success.

Start by defining realistic tasks from actual business scenarios, build a test suite reflecting your own systems and data, then measure task completion rate, failure recovery rate, step count, and execution time instead of relying only on public benchmarks.

Yes, if they are deploying agents into real workflows. Without task-based evaluation, organizations ship agents without reliable knowledge of their actual success rate — a direct operational and reputational risk.