Tone-only grading
Polite language can mask wrong refunds, missing disclosures, or unfinished troubleshooting.
AI customer support agent evaluation
Evaluate AI customer support agents on real tickets and outcomes.
Check whether the agent resolves the issue, uses the right account information, follows your policies, and hands off when needed. We review real tickets and test changes on similar cases.
A polite reply can still leave the problem unresolved.
Support tests need to check what happened to the customer's request, not just how the reply sounded.
Conflicting support rules
Adding exceptions to macros or prompts can cause new mistakes. Test the other request types after each change.
Unused review notes
Save approved edits as examples instead of leaving them buried in individual tickets.
Containment without quality
Closing a chat is not success if the customer returns, escalates, or gets an unsafe answer.
Support evaluation that produces reusable fixes
We reconstruct support traces or ticket threads, score against a support rubric, capture approved resolutions, and test retrieval on repeated intents.
Support scoring rules
Check whether the issue was resolved, the reply used accurate information, policy steps were followed, and handoffs happened on time.
Approved reply library
Save corrections approved by your support team so the AI can look them up on similar requests.
Support results
Compare resolution, human edits, handoffs, handling time, and repeat-contact risk for one type of request.
How to evaluate an AI customer support agent
Check whether the agent solved the right problem and followed the required steps. For failed cases, ask a reviewer to record the correct reply or actions.
Support outcome rubric
Define success per intent: required verification, allowed actions, resolution evidence, and when escalation is correct.
Policy and knowledge checks
Flag invented policy, outdated macros, missing disclosures, and answers that ignore account or order state in the trace.
Escalation and handoff quality
Score whether the agent escalates on time, with the right summary, and without abandoning the customer mid-flow.
See our LLM evaluation serviceConvert approved support replies into golden datasets for AI agents
Support review and results
Check whether the customer's problem was resolved correctly, not just whether the reply was polite.
Ticket scorecard
Check resolution, policy, factual accuracy, handoffs, and whether the customer is likely to need support again.
Containment vs resolution
Closing a chat is not success if the customer returns, escalates, or receives an unsafe answer.
Policy and fact checks
Flag invented policy, outdated saved replies, missing notices, and ignored account information.
Input requirements
Ticket or conversation export, policy version, tool results, and supervisor notes where available.
Sample scored ticket
Use the downloadable scorecard row as a starting template; replace every value with your own review.
Free template
Download the customer support scorecard
A CSV template for reviewing resolution, policy, factual accuracy, handoffs, and the risk that a customer needs to contact support again.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- CX and support leaders shipping AI agents on live queues
- Teams with repeating intents where humans already rewrite agent replies
- Support tasks with strict policy requirements, such as billing, cancellations, account changes, and required notices
- Ops teams who need measured improvement on one workflow before wider rollout
Sprint deliverables for support agents
Review
Review tickets, group recurring problems, and agree how to score a successful resolution.
Prepare examples
Record approved resolutions and keep separate tickets for testing the agreed fix.
Evaluate
Compare before-and-after results and agree what must pass before deployment.
Test one type of request first
We compare results on one support task and check other request types for new failures before recommending a wider rollout.
Limitations
- Tone-only QA misses wrong refunds, unfinished troubleshooting, and weak handoffs.
- Counting calls kept away from human agents can hide unresolved customer problems.
- Save approved supervisor corrections as examples if you want to reuse them outside the original ticket.
- Policy-sensitive flows need reviewer authority before corrections enter retrieval.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We reconstruct support traces or ticket threads, score against a support rubric, capture approved resolutions, and test retrieval on repeated intents.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Book a support-agent evaluation call.
Discuss recurring mistakes in your support tickets. We'll agree data handling before you share customer conversations.