Sampling 2% of tickets and calling it quality assurance was always a workaround for the cost of human review. Once an AI agent is resolving most of your volume, that 2% becomes a blind spot the size of your business.
Monitoring and grading AI support resolutions means defining what a correct resolution is for each workflow, checking every resolved ticket against those standards, detecting failures in near real time, and feeding the failures back into the agent so they stop recurring. In 2026 the practical bar is 100% coverage, not a sampled audit, because an AI agent can resolve tickets faster than a QA team can read them.
Resolution is not a status flag. A ticket marked solved can still be wrong, non-compliant, or quietly damaging. Grading checks correctness, not closure.
Define resolution per workflow. A KYC unlock, a refund, and a password reset each have different success criteria, and a single CSAT score hides all of them.
100% coverage is now affordable. Automated graders review every ticket at roughly $0.10 each, versus the $1.25 to $4 a human reviewer costs per ticket touched.
Near-real-time detection beats the monthly QA readout. The faster you catch a recurring failure, the fewer customers it reaches.
Grading should cover human and AI resolutions on the same rubric, so you can compare them honestly instead of grading the AI more harshly than the humans it replaced.
Last updated: June 2026
Most support teams inherited their QA process from a human-only world. A reviewer pulled a small random sample, scored it against a scorecard, and reported a number to leadership once a month. That made sense when reading a ticket cost a person ten minutes and you could not afford to read them all. It stops making sense the moment an AI agent is resolving thousands of tickets a week, because the sampled 98% you never look at is where the regulator-attention failures hide. This guide walks through how to set up resolution monitoring that grades every ticket, detects problems quickly, and closes the loop so the same failure does not happen twice. It applies whether the resolution came from an AI agent, a human, or a handoff between them.
What does it mean to monitor and grade AI support resolutions?
Monitoring AI support resolutions is the practice of evaluating the quality of each resolved ticket against a defined standard, surfacing the ones that fall short, and routing those findings back into the system that produced them. Grading is the scoring step: each resolution gets judged on whether it was correct, complete, compliant, and well-communicated, rather than simply whether the ticket was closed.
The distinction that matters is between closure and correctness. A ticket status of "resolved" tells you the conversation ended. It does not tell you whether the customer got the right answer, whether the agent followed the disclosure your compliance team requires, or whether a refund was issued for the right amount. Closure is a workflow event. Correctness is a quality judgment, and it is the thing grading exists to measure.
Resolution: A customer issue that has been fully addressed according to the success criteria defined for that workflow, not merely a ticket whose status changed to closed.
Grading rubric: The set of criteria a resolution is scored against, for example factual accuracy, policy adherence, required disclosures, tone, and whether the correct action was taken in connected systems.
In a traditional contact center, grading was done by QA analysts sampling a few percent of tickets. With AI resolving the bulk of volume, the same sampling logic leaves the vast majority of resolutions ungraded. The shift in 2026 is from sampled human review to automated grading that covers everything, with humans reviewing the exceptions the grader flags.
Why sampled QA breaks down for AI support
Sampled QA was a budget compromise, not a best practice. A human reviewer scoring tickets at $1.25 to $4 of fully loaded cost per ticket touched can only read so many, so teams settled for 2% to 5% coverage and extrapolated. That extrapolation assumes failures are evenly distributed. They are not. The tickets that hurt a regulated business, a mishandled dispute, a missing disclosure, a wrong KYC decision, are rare and clustered, which is exactly the profile a random 2% sample is worst at catching.
AI changes the math in two directions. Volume goes up, because an agent resolving a high share of tickets autonomously produces far more closed tickets per day than a human team did. And the failure modes change, because an AI agent can fail confidently and consistently. A human agent who misreads one policy makes one mistake. An AI agent that misreads one policy makes that mistake on every matching ticket until someone catches it. Sampled QA can let that run for weeks before a flagged ticket happens to land in the sample.
The practical consequence is that coverage and speed both have to improve. You need to grade every resolution, not a slice, and you need to catch a systematic failure in hours rather than at the end of a monthly QA cycle. Neither is achievable with human reviewers alone at any reasonable cost, which is why automated grading became the default approach for teams running AI at scale.
Step 1: Define what resolution means for each workflow
You cannot grade against a standard you have not written down. The first step is to define, per workflow, what a correct resolution looks like. A password reset is resolved when the customer can log in. A card dispute is resolved when the dispute is filed correctly, the provisional credit rules are applied, and the customer is told what happens next. A KYC unlock is resolved when identity is verified to the required standard and the account is actually unlocked, not when the agent says it will be.
Write these criteria as something a grader can check. For each workflow, specify the outcome the customer needed, the actions that had to happen in connected systems, any disclosures or policy steps that were mandatory, and the conditions under which the ticket should have been escalated instead of resolved. Vague criteria like "customer is satisfied" are not gradable. Specific criteria like "refund issued matches the disputed amount and the chargeback disclosure was sent" are.
This is also where you decide what counts as a resolution for accounting purposes. At Lorikeet the customer defines what counts as a resolution and holds a veto on it, and escalations to a human are not charged as resolutions. Tying the grading definition to the billing definition keeps the incentives honest: you are not paying for, or congratulating the agent on, tickets it punted to a person.
A useful test for whether a definition is gradable: hand it to someone who has never seen the workflow and ask them to score five real tickets. If two reasonable people score the same ticket differently, the definition is too loose and needs a concrete criterion they can both point to. Most ambiguity comes from edge cases, partial resolutions, tickets the customer abandoned, tickets resolved in a way that technically worked but left the customer confused, so write down how those should score before you start grading at volume rather than discovering the gaps in your data later.
Step 2: Build a grading rubric the grader can apply consistently
A rubric turns your per-workflow definitions into scoreable dimensions. A workable rubric for regulated support usually covers five things: factual accuracy against your knowledge base, policy and compliance adherence, whether the correct action was taken in connected systems, communication quality and tone, and whether escalation happened when it should have. Each dimension gets a clear pass or fail or a small scale, not a single blended number that hides which dimension failed.
The reason to keep dimensions separate is diagnostic. A blended 8 out of 10 tells you nothing actionable. "Factually correct, took the right action, but skipped the required disclosure" tells you exactly what to fix and which team owns it. When you later aggregate scores, you can still report a headline number, but the underlying dimensions are what drive remediation.
Apply the same rubric to human-handled tickets. If you grade the AI on disclosure adherence but never check whether your human agents send the same disclosure, you are not running quality assurance, you are running a bias machine. Grading both on one rubric is the only way to make a fair claim about whether the AI is better, worse, or equivalent on the work that matters.
Step 3: Grade 100% of resolutions, not a sample
With definitions and a rubric in place, the goal is full coverage. An automated grader, an AI evaluating the AI, reads every resolved ticket and scores it against the rubric. This is the step that was economically impossible with human reviewers and is now routine: grading every ticket at roughly $0.10 each is cheaper than having a person review even a fraction of them at $1.25 to $4 apiece.
Full coverage changes what your quality data can tell you. Instead of a sampled estimate with wide error bars, you get the actual failure rate per workflow, the actual distribution of failure types, and the ability to find every instance of a specific problem rather than the few that landed in a sample. When a regulator or an internal auditor asks how many times the agent gave a particular wrong answer last quarter, the answer is a query, not a guess.
Lorikeet Coach is the component that does this in our platform. It runs automated QA on 100% of tickets, scores resolution quality, and runs root-cause analysis on the failures, at around $0.10 per ticket. Coach can run standalone, grading the output of human agents or another vendor's AI, not only Lorikeet's own Concierge, which matters if you want one grading layer across a mixed support operation.
Step 4: Detect failures in near real time
Coverage without speed still lets bad resolutions reach customers. The point of grading every ticket is to catch a systematic failure while it is small. If the agent starts misapplying a policy after a knowledge base edit, you want that surfaced within hours, when it has affected ten customers, not at month end when it has affected ten thousand.
Set up near-real-time monitoring on the grading output. Watch for spikes in a specific failure dimension, drops in pass rate on a particular workflow, and clusters of low scores tied to a recent change. Alert on those signals the way an engineering team alerts on error rates. The grading layer becomes an early-warning system, not just a reporting one. The leading indicator you care about is the failure rate trend per workflow, because a sharp move there is usually a configuration or knowledge problem you can fix at the source.
Tie the alert thresholds to the change events that tend to cause regressions. A knowledge base edit, a new workflow version, a guardrail adjustment, and a model update are the usual suspects, so watch grading scores most closely in the hours after one of those ships. Treating a knowledge edit like a deploy, something that can break production and should be watched, is the mental shift that separates teams who catch failures early from teams who learn about them from a customer complaint. The same monitoring that catches AI regressions will also surface process drift on the human side, for example a new disclosure requirement that agents have not absorbed yet, which is another argument for grading both populations through one layer.
Step 5: Close the loop with a remediation process
Grading that does not change anything is just expensive observation. The value comes from the remediation loop: each flagged failure has to lead to a fix, and the fix has to be verifiable. For an AI agent, that usually means tracing the failure to its cause, a gap in the knowledge base, an ambiguous workflow instruction, a missing guardrail, correcting it, and then confirming the correction with a test before it ships.
This is where root-cause analysis earns its place. A flag that says "this ticket was wrong" is a start. A diagnosis that says "this ticket was wrong because the refund policy article contradicts the disputes workflow" is what lets you fix the class of failure, not just the instance. Lorikeet pairs grading with simulation-based validation, so a proposed fix can be run against historical and red-team scenarios before it reaches production. You change the workflow, prove the change resolves the failure without breaking something else, then deploy.
Close the loop for human resolutions too. If grading shows a recurring human error, the remediation is coaching or a process change rather than a workflow edit, but the loop is the same: detect, diagnose, fix, verify. A grading program that only ever corrects the AI quietly assumes the humans are fine, which the data rarely supports.
Make the loop auditable. Each flagged resolution should carry a record of what was wrong, what the diagnosed cause was, what changed, and the evidence the change worked. For a regulated business that record is not bureaucratic overhead, it is the artifact you hand an examiner who asks how you caught and corrected a problem. The same trail that proves diligence to a regulator also tells your own team whether remediation is keeping pace with detection: if flagged failures keep recurring after a supposed fix, either the diagnosis was wrong or the fix was never verified, and the audit trail is where you see that pattern. A grading program without this record can tell you something went wrong but not whether you actually fixed it, which is the question that matters most once the agent is handling regulated volume.
Grading human and AI resolutions on the same standard
A monitoring program scoped only to the AI answers the wrong question. Leadership does not actually want to know whether the AI is perfect, it wants to know whether moving work to the AI improves or degrades quality versus the human baseline. You can only answer that if both are graded on the same rubric, by the same grader, on the same workflows.
Running one grading layer across both also removes a common failure of AI rollouts: the double standard where every AI mistake is escalated and every equivalent human mistake is invisible because no one was grading the humans at 100% either. When the AI and the human team are held to identical criteria, the comparison is fair, and the cases where the AI underperforms become specific, fixable findings rather than a vague sense that the AI is risky.
The honest limitation here is that defining the rubric is real work, and a grader is only as good as the criteria it applies. Automated grading does not remove the need for human judgment about what correct means in your business. It removes the need for humans to read every ticket, and it makes the judgment they do provide go further. If your success criteria are wrong, full-coverage grading will simply enforce the wrong standard very consistently.
Where Lorikeet fits
Lorikeet builds AI concierges that resolve support issues end-to-end for complex and regulated businesses, and Lorikeet Coach is the analytics and QA layer that grades those resolutions. Coach runs automated quality assurance on 100% of tickets at around $0.10 each, produces a ticket quality score, runs root-cause analysis, and verifies resolutions, an AI evaluating the AI. Because Coach can run standalone, you can use it to grade human agents or another vendor's AI, not only Lorikeet's own Concierge.
The rest of the platform exists to close the loop the grading opens. Workflows are configured in plain English as natural-language and deterministic structured flows, guardrails check messages on the way in and on the way out, and simulation-based validation lets you prove a fix before it ships. That is the full monitor-grade-detect-remediate cycle in one place rather than a grading dashboard bolted onto an agent that cannot act on what it learns. If you are setting up resolution monitoring, see how Lorikeet Coach grades resolutions end to end.
Key Takeaways
Grade correctness, not closure. A resolved status means the conversation ended, not that the customer got the right and compliant answer.
Define resolution per workflow with gradable criteria, and tie that definition to what you actually pay for so the incentives stay honest.
Move from sampled QA to 100% coverage. Automated grading at roughly $0.10 per ticket makes full coverage cheaper than reviewing a sample by hand at $1.25 to $4 per ticket.
Detect failures in near real time so a systematic error is caught after ten customers, not ten thousand, then close the loop with root-cause analysis and a verified fix.
Grade human and AI resolutions on the same rubric. It is the only way to fairly compare them and the only way to avoid quietly holding the AI to a stricter standard than the humans it replaced.
