Traditional QA software still relies on humans sampling a handful of interactions. AutoQA platforms score every conversation automatically, enabling humans to focus on more valuable activities, like coaching, calibration and data deep-dives.

Do these platforms score AI agent conversations, or just human ones?

It varies. Some are still built purely around human agent transcripts; others, like evaluagent, apply the same quality framework across human, AI, and blended interactions.

Is AutoQA accurate enough to replace manual scoring entirely?

For most teams it's less about replacing manual QA outright, and more about using automation to cover every interaction, then pointing human review time at the conversations that actually need it. Human-in-the-loop is still a vital part of QA, even with automation.

What should I look for in a platform overview before I trial something?

Whether scoring is explainable — can you see why a conversation got the score it did — whether it covers your specific channels, and whether it's a standalone layer or expects you to replace what you already use.

Does evaluagent replace my existing contact center platform?

No — evaluagent sits on top of platforms like the ones covered here, as an independent quality and Conversation Intelligence layer, rather than replacing your contact center software itself.

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