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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMeasure an AI support agent by whether it resolves customers’ underlying problems—not merely by whether it keeps conversations away from people or completes an assigned task. A useful scorecard pairs verified resolution with conversation quality, customer experience, appropriate escalation, and technical reliability. Define the intended outcome first, then compare results with a relevant baseline and review failures by channel, language, use case, and knowledge source.
Start by defining what success means
Before building a dashboard, write a one-sentence success condition that specifies the customer outcome, the signal that will measure it, and the group or use case it applies to. Salesforce’s template is: “This agent succeeds when [outcome], as measured by [signal], for [who].” Salesforce Help
For example, a billing agent might succeed when a customer’s billing issue is confirmed resolved, measured by a resolution signal and a satisfaction measure, for routine billing inquiries. Safe escalation for account-specific or uncertain cases would be a guardrail. This is an application of the template, not a reported study result. Choose the few measures most closely tied to the agent’s purpose instead of treating every available dashboard metric as a priority.
Keep the metric families separate
No single rate can show whether an agent is helpful, safe, pleasant to use, and technically dependable. Choose a small set of primary KPIs, then use supporting measures to explain trade-offs and investigate changes.
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| Metric family | Measures to consider | What the measures tell you |
|---|---|---|
| Customer outcome | Verified resolution; unresolved or abandoned conversations; repeat contact about the same issue | Whether the underlying problem was fixed and whether that fix held. Salesforce identifies the underlying issue as the focus of resolution and includes abandonment and return or repeat rate among outcome measures. Salesforce Help |
| Automation and routing | Containment or deflection; assisted escalation; escalation rate; completed handoff | How much work stayed automated and whether the agent involved a person appropriately. These routing measures do not, by themselves, establish customer success. Zendesk distinguishes assisted escalation, contained resolution, and verified resolution in its reporting. Zendesk Help |
| Customer experience | CSAT or another feedback signal; customer effort where measured; re-prompting or repeated questions | How the interaction felt and how much work the customer had to do. Interpret satisfaction alongside the number of customers asked and the response rate; Zendesk reports ratings requested separately from ratings given. Zendesk Help |
| Conversation quality and policy | Accuracy; relevance; groundedness; instruction adherence; privacy, security, and policy compliance; appropriate refusal or escalation | Whether the answer was useful and stayed within approved bounds. A completed task does not prove that the response was correct or appropriate. |
| Operational health | Turn and retrieval latency; availability; timeouts and errors; throughput; incidents and guardrail events | Whether the agent is usable and operating within configured limits. Salesforce includes performance, availability, escalation, and guardrails among health and security measures. Salesforce Help |
| Business impact | Cost per successfully resolved issue; human workload or capacity; relevant downstream outcomes | Whether deployment changes the business outcome it was meant to affect. Define the calculation locally and compare equivalent workloads; the sources cited here do not establish a universal cost formula. |
Do not confuse resolution, containment, and task completion
These labels answer different questions and should not be used interchangeably:
- Verified resolution: Was the customer’s underlying issue fully resolved? Salesforce explicitly distinguishes solving the user’s problem from completing the agent’s assigned task. Salesforce Help
- Containment or deflection: Did the conversation proceed without human involvement? The customer may still leave without a solution.
- Task completion: Did the agent perform its assigned action? The action may be completed even if the customer’s broader problem remains.
- Escalation: Was a human brought in, and did the handoff transfer the case and context successfully? An escalation can be the right outcome when the issue needs human judgment.
Zendesk’s reporting distinguishes assisted escalation, contained resolution, and verified resolution. Preserve the product’s labels and definitions when interpreting platform reports rather than assuming a similarly named metric means the same thing elsewhere. Zendesk Help
Define each KPI and its denominator
A rate is only useful when readers can tell what was counted. For each measure, document the unit of analysis, eligible population, numerator, denominator, time window, exclusions, and data owner. State whether the unit is a session, conversation, ticket, issue, or customer.
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Also separate interaction-level resolution from customer-level repeat contact. A conversation can be marked resolved while the same customer returns about the same issue later; those are different observations and may have different time windows. Zendesk’s legacy AI metrics dataset defines “% Resolution rate” as automated resolution volume divided by conversation volume. That is a platform-specific formula, not a universal definition. Zendesk Help
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Establish a baseline for comparable contact types using the current support process or an appropriate human comparison. During rollout, monitor the same definitions and guardrails used before deployment. NIST’s AI Risk Management Framework Playbook recommends post-deployment monitoring, comparison with human or manual baselines, and tracking response quality, feedback, errors, and logs that support investigation. NIST AI RMF Playbook
Targets should reflect the agent’s purpose, the baseline, and the consequences of failure. Zendesk publishes the following suggested target ranges in its AI-agent training guidance; they are vendor guidance, not independently established cross-industry standards. The page does not state a publication year.
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| Measure | Zendesk’s suggested target |
|---|---|
| Resolution rate | 60–80% |
| Deflection rate | 40–60% |
| Answer accuracy | 85–95% |
| Confidence score | 70–90% |
| Average conversation length | 3–5 turns |
| Customer satisfaction | 4.0+ out of 5 |
| Escalation rate | 20–40% |
These ranges are starting context, not automatic targets for every support workflow. For example, a high escalation rate may be expected where cases are sensitive or require account-specific judgment; a low rate is not evidence of good service if unresolved contacts are increasing. Zendesk Help
Review real conversations to explain the numbers
Dashboards identify patterns; conversation review helps explain them. Use explicit criteria that reviewers can apply to evidence in the interaction, such as:
- Whether the agent understood the customer’s intent.
- Whether the answer was factually accurate, relevant, and supported by approved knowledge.
- Whether instructions and policy were followed, including privacy and security requirements.
- Whether the response was clear and avoided unnecessary repetition.
- Whether the agent escalated when needed and transferred useful context.
Review both a representative sample and a failure-focused sample. Look at unresolved or abandoned contacts, repeat answers, negative feedback or sentiment, and handoffs that did not work. Record the failure reason so the corrective action can target the right cause: knowledge content, retrieval, policy, workflow, escalation design, or system reliability.
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Zendesk documents conversation scorecards and dashboards for reviewing AI-agent performance; its BotQA dashboard includes escalation, repeated-answer, low communication-efficiency, and negative-sentiment signals. Zendesk Help Automated scoring can help surface cases, but it should not be treated as ground truth: calibrate it against human-reviewed examples and inspect disagreements. NIST recommends tracking feedback, logs, errors, and response quality, and comparing performance with human baselines. NIST AI RMF Playbook
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Segment results and compare like with like
A blended rate can hide a weak flow or an uneven customer experience. Where the data allows, break results down by:
- Channel
- Language
- Use case or contact reason
- Knowledge source
- Agent or workflow
Compare equivalent traffic and cases, and document differences in populations or metric definitions. When comparing an AI workflow with a human or existing process, account for differences in case mix rather than treating unlike contacts as a fair comparison. Zendesk’s reporting documentation describes segmentation by agent, channel, language, use case, and knowledge source. Zendesk Help
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Turn findings into corrective action
Assign an owner to each material failure pattern and tie the action to its likely cause. A recurring unsupported answer may require better knowledge content or retrieval; an unsafe response may require a policy or workflow change; a failed handoff may call for revised escalation conditions or better context transfer; timeouts call for reliability work. After the change, measure the same defined outcome and guardrails so the team can see whether the fix improved performance or shifted the problem elsewhere. NIST’s Measure guidance supports post-deployment monitoring, feedback, error-response measurement, and logs that help diagnose failure sources. NIST AI RMF Playbook
Frequently Asked Questions
What is the most important KPI for an AI support agent?
There is no single best KPI for every agent. Choose the measure that directly captures the intended customer outcome—often verified resolution—and pair it with guardrails for quality, safety, experience, and appropriate escalation.
Is deflection the same as resolution?
No. Deflection or containment means a conversation did not involve a human; resolution means the customer’s underlying problem was solved. Track them separately.
What is a good AI support resolution rate?
Zendesk suggests 60–80% in its AI-agent training guidance, but that is vendor guidance rather than a neutral industry standard. Set a local target using a comparable baseline, the use case, and the risk of an incorrect or incomplete answer.
How should I measure AI chatbot CSAT?
Track the satisfaction result alongside how many customers were asked and how many responded. Report the survey method and response rate so readers can judge how representative the result may be.
How often should AI support agent performance be reviewed?
Monitor operational and quality signals after deployment, and establish a regular review cadence suited to the service’s risk and volume. Investigate material changes and recurring failure patterns rather than relying only on a periodic aggregate score.
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