Tests For Elevenlabs Agents

A voice agent can break without anyone touching its code: a reworded prompt, a new tool or a changed workflow can quietly alter how it handles calls it used to get right. ElevenLabs introduced Agents Testing in a September 9, 2025 post as a way to catch those regressions by running structured simulated conversations against an agent before changes go live.

The post names four things tests can check: whether external tools are triggered with the right parameters, whether handoffs to a human work, whether multi-step workflows complete, and whether guardrails keep the agent on-brand. Teams can write tests for critical flows or generate them from past customer conversations, and ElevenLabs says tests can run in CI/CD pipelines so every pull request is checked against the whole suite.

The current testing documentation shows how the judging works. Tests come in three types: full multi-turn simulations against a simulated user, checks of the agent's next reply given a conversation so far, and tool-call checks. Tool parameters can be matched exactly or by pattern, but next-reply results and semantic parameter checks are decided by an LLM evaluator against criteria the team writes. Because agent outputs vary between runs, the docs recommend repeating a test to get a pass rate, so a passing suite is evidence of behavior under the scenarios written, not a guarantee. An earlier ElevenLabs post on testing its own documentation assistant describes the same ideas, conversation simulations, tool mocking and CI runs, applied in-house before this feature launched.