AI support agent claims: resolution rate, deflection, accuracy, handoff, and buyer questions
Last reviewed May 30, 2026
Support-agent marketing often mixes resolution rate, deflection rate, answer accuracy, hallucination rate, source grounding, and handoff quality in one headline. This guide turns those public claims into evidence requests a buyer can use before relying on the wording. For handoff or expert-oversight reassurance alone, see the human review boundary guide.
Evidence buyers verify
- The exact support-agent accuracy, hallucination, source-grounding, resolution-rate, or deflection-rate claim.
- A test set based on comparable customer questions, support channels, languages, and policy complexity.
- A scoring rubric that separates factual correctness, source grounding, completeness, tone, action completion, and handoff quality.
Opens the checker for this claim type. Paste your vendor's exact wording there. Evidence questions only — not a blacklist or fraud detector. Not sure what a result looks like? See a sample receipt.
Sources this guide draws from
- · January 26, 2023
Official source for validity, reliability, measurement, and trustworthy AI evaluation context.
- · Accessed May 24, 2026
Public company source for hallucination-rate, support-content, handoff, and customer-facing answer wording; used as claim wording evidence, not independent validation.
- · Updated over a week ago, accessed May 24, 2026
Public company source for retrieval, grounding, response validation, and support-content source-of-truth wording; not a third-party accuracy assessment.
- · Accessed May 24, 2026
Public company source for AI agent resolution, connected-knowledge, QA, and autonomous workflow wording; used to inspect claim scope, not independent validation.
- · Accessed May 24, 2026
Public company source for 80%+ resolution, productivity, and operational-efficiency AI support wording; not independent outcome evidence.
- · Last updated May 5, 2026
Public company source for resolution-rate definition, repeat-contact risk, error-rate, and outcome-quality measurement language; not independent outcome validation.
Public claims with documented evidence gaps
"very low hallucination rate (<1%)"
Accuracy / Performance- Source and date
- Intercom Fin AI Agent FAQs · Accessed May 24, 2026
- Evidence signal
- Hallucination-rate wording without the evaluation set, sampling method, severity rule, or production monitoring boundary in the claim.
- Evidence gap
- A buyer needs the answer sample, source-grounding rubric, unsupported-claim definition, time period, support channel, and escalation rule.
- Buyer question
- For the hallucination-rate claim, what sample of support conversations was reviewed and how were unsupported answers counted?
"only provides answers based on your support content or data"
Accuracy / Performance- Source and date
- Intercom Fin AI Agent FAQs · Accessed May 24, 2026
- Evidence signal
- Source-grounded answer wording that depends on retrieval quality, source freshness, user access rules, and fallback behavior.
- Evidence gap
- A buyer needs the source list, sync cadence, retrieval test, access-control boundary, no-answer behavior, and answer audit trail.
- Buyer question
- For the support-content-only answer claim, which sources can the agent use and what happens when no current source supports the answer?
"resolve 80%+ of customer and employee interactions instantly across any channel"
ROI / Outcome- Source and date
- Zendesk AI for customer service page · Accessed May 24, 2026
- Evidence signal
- High resolution-rate wording that combines volume, speed, channel scope, and outcome quality.
- Evidence gap
- A buyer needs the resolution definition, denominator, excluded interactions, repeat-contact rate, CSAT, wrong-answer rate, and channel mix.
- Buyer question
- For the 80%+ resolution claim, how is a resolved interaction defined and which channels or cases are excluded?
"Evaluate every interaction with automated QA controls"
Compliance / Safety- Source and date
- Zendesk AI agents page · Accessed May 24, 2026
- Evidence signal
- Every-interaction QA wording that may not name the scoring rubric, reviewer boundary, false-pass risk, or audit process.
- Evidence gap
- A buyer needs the QA score definition, sampling or full-coverage proof, escalation threshold, human review boundary, and quality-history export.
- Buyer question
- For the every-interaction QA claim, what quality checks are automated and when does a human review the support-agent answer?
Match each claim pattern to the evidence buyers need
| Claim pattern | Evidence needed | Buyer question |
|---|---|---|
| AI customer support agent accuracy percentage | Known-answer test set, support topic mix, source freshness, scoring rubric, sample size, time period, and reviewer qualification. | Were the tested questions drawn from our real support queue, and how were partial or outdated answers scored? |
| Hallucination-free, low-hallucination, or no hallucination claim | Unsupported-claim definition, groundedness check, source citation rule, severity tiers, audit sample, and production monitoring cadence. | What counts as a hallucination in this support workflow: wrong policy, stale source, missing caveat, or invented system state? |
| Grounded answer, source-cited answer, or RAG accuracy claim | Retrieval evaluation, source rank evidence, citation support check, access-control rule, source-sync cadence, and fallback behavior. | Can the vendor show which source supported each answer and how unsupported questions are blocked or handed off? |
| Resolution rate, deflection rate, or automated support outcome | Resolution definition, denominator, excluded cases, repeat-contact rate, escalation rate, CSAT, and error-rate review. | Does the resolution metric include customers who returned, escalated later, abandoned the chat, or received an incomplete answer? |
| Automated QA, every-interaction review, or quality scoring | QA rubric, scoring method, reviewer boundary, false-pass handling, sampled transcript export, and remediation process. | Which quality failures are automatically detected, and which still require human review of the AI support-agent transcript? |
Evidence to request
- The exact support-agent accuracy, hallucination, source-grounding, resolution-rate, or deflection-rate claim.
- A test set based on comparable customer questions, support channels, languages, and policy complexity.
- A scoring rubric that separates factual correctness, source grounding, completeness, tone, action completion, and handoff quality.
- Production monitoring for wrong answers, stale sources, repeat contact, escalation, CSAT, and unresolved tickets.
- A handoff and fallback rule for unsupported questions, low-confidence answers, outages, sensitive topics, and tool-action failures.
Questions to put in front of the vendor
- For this support-agent claim, what exact metric is being claimed: resolution rate, deflection rate, factual correctness, groundedness, or QA score?
- What source content, tickets, policies, and customer records can the agent use, and how often are those sources refreshed?
- How does the vendor count a wrong answer, unsupported claim, stale policy answer, incomplete resolution, or later repeat contact?
- Which questions are blocked, clarified, or handed to a human instead of being answered by the AI agent?
- What wording boundary should replace the claim if the evidence only covers one support queue, one language, one channel, or one customer segment?
Wording boundaries to compare against
- Reported accuracy is based on a named support-topic test set with documented scoring rules and source-grounding checks.
- The AI agent answers from selected support content and hands off when no current source supports a confident answer.
- Resolution rate is measured on defined support queues and excludes repeat contacts, escalations, and incomplete outcomes.
- Automated QA reviews selected answer attributes; human review remains required for sensitive or disputed cases.
Have your vendor's exact claim wording ready?
Check an AI support agent resolution or accuracy claim How the evidence method works