Intel / article

Testing Conversational Ai Agents

Source: elevenlabs.io
Author: ElevenLabs editorial sitemap
Date: Published Sep 28, 2026

Why it matters

Shows a concrete pattern for testing production agents: define failure criteria, simulate whole or mid-conversation scenarios with mocked tools, and run them in CI. The same loop applies to any agent, not just voice.

Key takeaways · AI-distilled

ElevenLabs defines a failed conversation as one where the gives incorrect information or does not help the user reach their goal, and scores calls on five criteria: valid interaction, user satisfaction, root-cause identification, resolution, and .
If the Interaction criterion fails, the call is treated as invalid rather than an agent failure; failures on the other criteria trigger investigation that leads to fixes such as tool-timing changes or new .
Partial simulations that start mid-conversation are the team's equivalent of unit tests: they isolate one decision point or sub-flow, use mocked tools, and are graded with the same criteria applied to production calls.
and simulation runs are wired into the GitHub CI/CD pipeline, so every update to the El documentation agent is tested before deployment, which the team says prevents regressions.

Terms in this piece · Glossary

AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
hallucination — When a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.
guardrails — The checks around a model that block bad inputs and outputs — filters, validators, and permission rules the model itself can't override.

Read the source elevenlabs.io