# Which AI Customer Support Agents Have the Best Accuracy and Fewest Hallucinations or Wrong Answers?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">None of these are hallucination-free, but the pattern across recent reviews is fairly consistent: accuracy tracks how well-grounded the agent is in real data, not the vendor's size.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/salesforce-agentforce/reviews"><strong>Salesforce Agentforce</strong></a>: when it's grounded in clean, unified CRM data, reviewers describe genuinely accurate, context-aware answers. The recurring complaint is that responses on complex logic can turn unpredictable, it sometimes ignores instructions it's already been given, and multiple reviewers flag that debugging why an answer went wrong is opaque. The consistent message is that output quality is capped by your underlying data quality, not just the model.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/fin/reviews"><strong>Fin</strong></a>: strong on straightforward, repetitive questions, reviewers consistently credit it with pulling accurate answers from the knowledge base. Its failure mode is different from Agentforce's, it's not wrong data so much as getting stuck looping on an answer instead of escalating, with a smaller number of reviewers noting it occasionally gives advice that doesn't match the customer's actual situation.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/jotform-ai-agents/reviews"><strong>Jotform AI Agents</strong></a>: accuracy here is almost entirely a function of training. Reviewers who structure their knowledge base as direct Q&amp;A pairs report solid results, while several others explicitly call out hallucination or off-topic answers, usually tied to a thin or disorganized knowledge base.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/retell-ai/reviews"><strong>Retell AI</strong></a>: different modality (voice, not chat), but worth flagging because its complaints don't cluster around wrong answers the way the others do, they're scattered (a setup issue here, an accent quirk there) rather than a shared accuracy problem.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If I had to rank these on hallucination risk alone, I'd put Retell AI and Fin ahead of Jotform and Agentforce, but only because the latter two's accuracy depends so heavily on how much setup work goes in first.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Has anyone actually measured a wrong-answer rate on one of these, or is "it depends on your knowledge base" the honest answer everyone's giving?</p>

##### Post Metadata
- Posted at: 6 days ago
- Author title: SEO Content Writer
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;We ended up building our own lightweight QA process instead of trusting a published accuracy number. Every month someone pulls a random sample of conversations and manually tags them right or wrong, which at least means the metric isn&#39;t self reported by whoever built the agent.&lt;/p&gt;

##### Comment Metadata
- Posted at: 5 days ago
- Author title: Marketing Executive





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