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AI support agents can resolve a substantial share of customer conversations without a human, but there is no single rate that applies to every business. In a four-week benchmark reported by SaaStr on October 6, 2026, the top five vendors averaged 70% automation, the median vendor reached 48%, and the conversation-volume-weighted rate was 46%. Those figures describe one test of ecommerce support agents—not a guaranteed result for your ticket queue or a current ranking.
What the 13-vendor snapshot found
Jason Lemkin, founder of SaaStr, reported results from a Gorgias-run test involving 13 vendors and 212 live, mid-market ecommerce stores. Each agent received the same customer messages adapted to the store. Responses were scored blind against 26 binary checks, with factual claims such as prices, policies, and SKUs checked against the live store. The reported automation rates cover the four weeks preceding the October 6, 2026 article.
| Vendor | Automation rate | Conversations in the test |
|---|---|---|
| Rep AI | 75% | 120 |
| Decagon | 73% | 34 |
| Yuma | 71% | 118 |
| Ada | 68% | 420 |
| Gorgias | 64% | 388 |
| DigitalGenius | 50% | 561 |
| Sierra | 48% | 408 |
| Siena | 46% | 300 |
| Kodif | 44% | 188 |
| Intercom | 42% | 307 |
| Zendesk | 37% | 467 |
| Envive | 22% | 499 |
| Klaviyo | 21% | 309 |
The five highest rates average 70%; the median is 48%, and weighting each vendor’s result by its conversation volume produces 46%. These are distinct summaries of the same four-week snapshot, not competing estimates of a universal AI resolution rate. SaaStr’s October 6, 2026 report provides the vendor results and test description.
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In Lemkin’s definition, “Automation rate here is the share of engaged conversations the AI resolved with no human involved.” A conversation counted as resolved only if it required no human involvement and was not deflected from the channel. That is one benchmark’s rule; vendor dashboards and contracts may use different definitions. The article also notes that Gorgias product reporting separately counts an AI-resolved interaction after 72 hours without human help.
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A high closure rate does not show whether the answer was correct, useful, or safe. The earlier test gave vendors blind quality scores on a 0–100 scale: Yuma and Sierra scored 72, Decagon 70, Gorgias and Intercom 66, Klaviyo 65, Rep AI 55, and Ada 39. In particular, Ada’s 68% automation alongside a 39 quality score illustrates why closure and answer quality should be checked together. These quality scores belong to the October snapshot and should not be treated as current ratings.
Results also varied by issue. In the same report, topic-level quality scores were 77.4 for returns policy, 74.4 for shipping policy, 63.5 for modifying or canceling an order, 60.1 for order tracking, 59.7 for damaged items, and 49.3 when a shopper was unsure what they needed. Common failure patterns included authentication barriers, repeatedly asking for order numbers, clarification loops, and unnecessary handoffs. These findings describe that benchmark, not every store or deployment.
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How the newer benchmark differs
Gorgias’s benchmark page, refreshed in October 2026, describes a larger and longer-running test: 18 vendors across 228 live Shopify stores, with 10,152 conversations captured and 10,150 judged blindly by an LLM. It describes a 90-day overall window, separates shopping-assistant and support-agent tasks, adapts shared questions to each store, uses cold browser sessions, and allows agents to initiate handoffs. Vendor rankings require at least 15 judged conversations per job.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteOn that page, Gorgias reports itself as #1 overall and #1 for support, with 68% support automation and a 74/100 support quality score. These are Gorgias’s own benchmark claims, not an independent replication, and they are not directly comparable with its 64% result in the earlier four-week snapshot. The newer page says its support composite weights automation at 50%, quality at 40%, and speed at 10%; the shopping composite weights those measures at 40%, 35%, and 25%, respectively. The operator says it applies the same test to itself and publishes its methodology, which makes the method more inspectable but does not remove its commercial interest as a ranked vendor. See Gorgias’s benchmark page and stated methodology.
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What determines the rate at your business
Your own result depends on the questions customers ask, the information the agent can access, and what actions it is permitted to take. A policy question may be answerable from approved store content, while tracking, damaged-item claims, identity checks, cancellations, and other requests can require live lookups or human judgment. Integrations, store configuration, and handoff behavior therefore matter alongside the model.
The October SaaStr report says nine of ten vendors with sufficient history lost automation in the preceding four weeks. It does not establish why: model changes, configuration drift, or a harder question mix were offered as possibilities, not demonstrated causes. Treat a vendor’s rate as a dated measurement, not a permanent product capability.
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- Handy In-line Controls: Simple in-line controls on the headset cable let you adjust the volume or mute calls without disruption
- Plug-and-Play USB Computer Headset: Simply plug the USB-A connector into your computer and you’re ready to talk or listen without the need to install software
- Padded Comfort: Comfortable headphones with adjustable headband features swivel-mounted, leatherette ear cushions for hours of comfort and is easy to clean
How to evaluate an AI support agent
- Get the resolution definition in writing. Ask what counts as a resolved conversation, when it is considered resolved, whether a human touch disqualifies it, and what makes a resolution billable. Do not assume the benchmark definition matches the product dashboard or contract.
- Test your actual ticket mix. Include the real balance of pre-sale and post-sale questions, with policy, order lookup, damaged goods, authentication, and requests that require changes to an order.
- Score quality and automation on the same conversations. Track whether responses are accurate and actionable, whether the issue was genuinely resolved, and whether a human had to intervene. Record speed too, but do not treat a fast answer as proof of a correct one.
- Exercise access and handoffs. Use cold sessions and live-store data where appropriate. Check how the agent handles authentication, order-number requests, unavailable information, action permissions, clarification loops, and escalation to a person.
- Repeat the test. Recheck after model, content, integration, or store changes, and across different periods. A benchmark rank or an early deployment result can move as questions and configurations change.
- Compare costs only after definitions align. Ask the vendor to identify the exact billable event and reporting window. The cited benchmark reports do not establish current contract prices or vendor-specific billing terms, so a benchmark automation rate alone cannot establish expected cost.
The author of the SaaStr article recommends using 65–70% as a business-case target. That is advice, not a forecast or guarantee for an individual support operation; judge feasibility against your own measured resolution quality, ticket mix, and operational requirements.
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