Most QA tools grade the conversation. The harder question is whether the resolution actually happened. A polite, on-tone reply that left the refund unprocessed is a failed ticket, no matter how it scored on rubric.
AI resolution grading is the practice of using AI to evaluate support ticket outcomes - did the issue get resolved, did the action complete, did the answer follow the standard operating procedure - across 100% of tickets instead of a 1-2% manual sample. In 2026 the leading tools have shifted from scoring tone and empathy toward verifying outcomes, and the most demanding buyers now grade the AI agent's own resolutions, not just the humans.
Manual QA historically covers 1-3% of tickets, per industry CX benchmarks. AI-driven QA can cover 100%, which changes what the score is for.
Resolution grading is distinct from conversation scoring: it checks whether the underlying task completed and matched the SOP, not just whether the reply was friendly.
As AI agents handle more volume, the urgent gap is grading the AI itself - "AI evaluating the AI" - so failed resolutions are caught before a customer or regulator does.
Auto-scoring against a custom rubric, root-cause analysis, and coaching insights are now the dominant evaluation criteria, ahead of dashboards.
For regulated teams, an auditable grade tied to the specific SOP step that passed or failed matters more than an aggregate CSAT proxy.
Last updated: June 2026
Quality assurance in support used to mean a team lead pulling a handful of tickets each week and filling in a scorecard. That model breaks twice over in 2026. First, sampling 1-2% of tickets tells you almost nothing about the long tail where the expensive failures hide. Second, when an AI agent is resolving the majority of your volume, the thing you most need to grade is the AI, and a human reviewer cannot keep pace. The tools below are ranked on one lens: how well they verify that a resolution actually happened and matched your process, not just whether the message read well. This is a buyer-neutral ranking based on shipping product, real coverage claims, and what the resolution-grading job actually requires.
What is AI Resolution Grading?
AI resolution grading is the use of large language models to evaluate support tickets against a rubric and verify the outcome: was the customer's issue resolved, did the required action (refund, account change, dispute filing) complete, and did the handling follow the standard operating procedure. Mature tools score 100% of tickets automatically and surface the specific reason a ticket passed or failed.
The category splits around what "quality" means. First-generation QA scores the conversation: tone, empathy, greeting, closing, adherence to a tone-of-voice rubric. That is useful for coaching humans but it is a proxy. A ticket can score perfectly on tone and still be a failed resolution because the agent promised a refund that never processed. Resolution grading checks the outcome and the process, not just the prose. The most advanced version of this is turning the grader on the AI agent itself, so every AI-resolved ticket is verified before it counts as resolved.
Resolution verification: Confirming that the ticket's underlying task actually completed and the customer's problem was solved, rather than scoring only the language of the reply.
Auto-scoring: Grading every ticket automatically against a configurable rubric (SOP adherence, resolution correctness, policy compliance) instead of a manual reviewer sampling a small percentage.
Lorikeet builds AI concierges that resolve complex, regulated tickets end-to-end, and a companion agent, Coach, that grades them. Coach runs 100% automated QA, scores every ticket, performs root-cause analysis, and verifies resolutions - the "AI evaluating the AI" pattern. It is deployable standalone over an existing human or AI support stack at roughly $0.10 per ticket, so teams can grade resolutions without replacing their helpdesk.
At-a-Glance Comparison
At a glance
Tool: Lorikeet Coach · Best For: Teams that need to verify resolutions and grade the AI agent itself · Key Strength: 100% automated QA with resolution verification and root-cause analysis · Pricing: ~$0.10 per ticket, standalone
Tool: Klaus (Zendesk QA) · Best For: Teams on Zendesk wanting auto-QA across humans and bots · Key Strength: AutoQA scoring of 100% of conversations · Pricing: Per-seat, quoted by sales
Tool: MaestroQA · Best For: Enterprises wanting deep, customizable scorecards and calibration · Key Strength: Granular rubric design plus AI auto-grading · Pricing: Custom (contact sales)
Tool: Loris · Best For: Conversation intelligence on top of QA scoring · Key Strength: Real-time sentiment and conversation insights · Pricing: Custom (contact sales)
Tool: Forethought · Best For: Teams wanting QA inside a multi-agent resolution stack · Key Strength: Agent QA alongside Solve, Triage, Assist · Pricing: ~$59.5K median annual
Tool: Zendesk QA · Best For: Zendesk Suite customers wanting native QA · Key Strength: Native Suite integration via the Klaus acquisition · Pricing: Add-on, per-seat
Tool: Decagon · Best For: Enterprises grading a high-end AI agent deployment · Key Strength: Built-in analytics on its own AI resolutions · Pricing: ~$400K median annual
What Resolution Grading Actually Needs
Most QA buying guides start with scorecard flexibility and dashboards. For resolution grading, those are downstream of a harder requirement: can the tool tell whether the resolution happened. The criteria below separate outcome-verifying tools from conversation-scoring ones.
Outcome verification, not tone scoring
The core test is whether the tool grades what happened to the customer's problem, not how the reply sounded. Did the refund process, did the address update, did the KYC unlock complete. A tool that only checks greeting, empathy, and closing is measuring a proxy. Ask whether the grade can fail a ticket that read perfectly but left the action incomplete.
Verification against SOPs
A correct outcome reached the wrong way is still a process failure in a regulated business. The grader needs to check the resolution against your standard operating procedure: was the disclosure read, was identity verified before the account change, was the escalation path followed. This is the difference between "the customer is happy" and "the customer is happy and we can defend how we got there."
100% coverage
Manual QA samples 1-3% of tickets. The expensive failures live in the other 97%. AI grading only earns its keep if it scores every ticket, because the point is to catch the rare, costly miss, not to re-confirm the average. Ask for the actual coverage percentage, not the headline "AI-powered."
Grading the AI agent
As AI agents resolve more volume, the urgent gap is grading the AI itself. A human reviewer cannot keep up with an agent resolving thousands of tickets a day, and the agent should not grade only itself with no second check. The strongest pattern is a separate evaluating agent - "AI evaluating the AI" - that verifies each resolution independently before it counts.
Root cause, not just a score
A number tells you a ticket failed. It does not tell you why, or whether the same failure is happening across hundreds of tickets. The tool should cluster failures, point at the responsible step (a missing knowledge article, a broken workflow branch, an under-specified SOP), and feed that back into improving the process. A grade without a cause is a report nobody acts on.
The 7 Best AI Tools to Grade Support Ticket Resolutions in 2026
1. Lorikeet Coach
Lorikeet Coach is the analytics and quality-assurance agent built for teams that need to verify resolutions, not just score conversations. It runs 100% automated QA, assigns a ticket quality score, performs root-cause analysis, and verifies that the resolution actually happened. Its defining capability is "AI evaluating the AI": Coach grades the resolutions produced by AI agents (Lorikeet's own concierge or another vendor's) so a failed resolution is caught before the customer or a regulator finds it. Most QA tools tell you a reply was polite. Coach tells you whether the problem was solved and whether the process was followed.
Key Features
100% automated QA: every ticket scored against a custom rubric, not a 1-3% manual sample.
Resolution verification: confirms the underlying task completed and the issue was actually solved, not just that the message read well.
"AI evaluating the AI": grades AI-agent resolutions independently, so the agent is not the only judge of its own work.
Root-cause analysis: clusters failures and points at the responsible step (knowledge gap, workflow branch, SOP gap) so the process improves.
Deployable standalone over an existing human or AI support stack, so you can grade resolutions without replacing your helpdesk.
Ideal For
Complex and regulated teams (fintech, financial services, healthtech, insurance) that need every resolution verified against SOPs and want to grade the AI agents handling their volume, not just the humans. Coach suits teams whose toughest stakeholder is compliance and who need an auditable reason a ticket passed or failed.
Pricing
Roughly $0.10 per ticket, available standalone. Because it grades on top of an existing stack, teams can start with QA before adopting Lorikeet's resolution agent.
Honest Limitation
Coach is built for complex, regulated workflows and the resolution-verification problem. A small team that only needs lightweight tone scoring on a low ticket volume may find a simpler conversation-QA tool a closer fit, and Coach is most powerful when paired with structured SOPs it can grade against.
2. Klaus (Zendesk QA)
Klaus, now part of Zendesk as Zendesk QA, popularized automated conversation scoring. Its AutoQA grades 100% of conversations against categories like tone, grammar, empathy, and resolution, and surfaces low-scoring tickets for review. It is one of the most mature auto-QA products and a sensible default for teams already on Zendesk.
Key Features
AutoQA scoring across 100% of conversations.
Coverage of both human and AI-agent conversations.
Calibration sessions and reviewer assignment workflows.
Native integration with Zendesk plus connectors to other helpdesks.
Sentiment and spotlight detection to flag tickets needing attention.
Ideal For
Teams on Zendesk that want broad automated coverage of conversation quality across humans and bots, with established calibration and reviewer tooling.
Pricing
Per-seat, quoted by sales as a Zendesk add-on. Pricing scales with the number of reviewed agents.
3. MaestroQA
MaestroQA is an enterprise QA platform known for deep, customizable scorecards and strong calibration tooling. It has layered AI auto-grading on top of its rubric engine, so teams can combine highly specific manual scorecards with automated scoring at scale. Its strength is configurability for teams that have invested in a detailed quality program.
Key Features
Highly granular, customizable scorecards and rubrics.
AI-assisted auto-grading layered on the rubric engine.
Calibration, appeals, and reviewer-agreement tracking.
Analytics that tie QA scores to coaching and performance.
Integrations across major helpdesks and contact-center platforms.
Ideal For
Enterprises with a mature QA function that want maximum control over scorecard design and calibration, and that are comfortable building the rubric logic themselves.
Pricing
Custom (contact sales). Typically enterprise annual contracts scaled to agent and reviewer count.
4. Loris
Loris is a conversation-intelligence platform that grew out of crisis-line text analysis and now serves CX teams with QA scoring, sentiment analysis, and conversation insights. Its strength is reading the emotional and topical signal across large volumes of conversations, which complements outcome grading with a view of customer experience.
Key Features
Automated QA scoring across conversations.
Real-time sentiment and emotion detection.
Conversation insights and topic clustering.
Coaching workflows tied to detected patterns.
Helpdesk and contact-center integrations.
Ideal For
Teams that want conversation intelligence and sentiment analysis alongside QA scoring, and that prioritize understanding customer experience signal across volume.
Pricing
Custom (contact sales). Scoped to volume and modules.
5. Forethought
Forethought offers a multi-agent platform (Solve, Triage, Assist, Discover, and Agent QA) where quality scoring sits inside the same stack that resolves and routes tickets. Zendesk announced its acquisition of Forethought in March 2026. For teams that want resolution and QA from one vendor, the integration is the draw; the trade-off is signing into Zendesk's post-acquisition roadmap.
Key Features
Agent QA module within a five-agent platform.
Quality scoring tied to the resolution and triage agents.
Natural-language business logic (Autoflows) rather than decision trees.
Multi-channel coverage across chat, email, voice, and more.
Gap analysis via the Discover agent to find missing content.
Ideal For
Mid-market and enterprise teams that want resolution, triage, and QA from one vendor and are comfortable being folded into Zendesk's roadmap.
Pricing
Median reported annual contract approximately $59,500, with a range of $40,000-$160,000 depending on modules and volume.
6. Zendesk QA
Zendesk QA is the native quality product built from the Klaus acquisition, embedded in the Zendesk Suite. For existing Zendesk customers it removes integration friction and brings AutoQA scoring directly alongside tickets. The honest read is that the cost is layered on top of Suite and AI add-on fees, and the grading lens is conversation quality rather than independent resolution verification.
Key Features
Native to the Zendesk Suite, no middleware for existing customers.
AutoQA scoring across 100% of conversations.
Reviewer assignment, calibration, and coaching workflows.
Spotlight detection for at-risk tickets.
Coverage of AI-agent and human conversations within Zendesk.
Ideal For
Zendesk Suite customers that want QA native to their helpdesk and can absorb the layered cost of Suite, AI add-on, and QA together.
Pricing
Per-seat add-on to the Zendesk Suite, quoted by sales and scaled to reviewed agents.
7. Decagon
Decagon is a high-end enterprise AI agent platform with built-in analytics on its own resolutions. For teams running Decagon as their resolution agent, those analytics give visibility into how the AI performed. It is included here as the QA layer for its own deployments rather than a standalone grader you point at another vendor's stack.
Key Features
Analytics and quality reporting on Decagon's own AI resolutions.
Voice, chat, and email coverage in one platform.
White-glove deployment with embedded engineering.
Production deployments processing large interaction volumes.
Per-conversation or per-resolution pricing models.
Ideal For
Large enterprises already running Decagon as their resolution agent that want native analytics on that deployment, rather than a vendor-neutral grader.
Pricing
No published rates. Industry data suggests a median total contract value near $400,000/year, with per-conversation or per-resolution fees.
Conversation scores tell you a reply was polite. Resolution grading tells you the problem was solved and the process was followed. See how Lorikeet Coach verifies every resolution.
How to Choose a Resolution-Grading Tool
The tools above split into two camps: conversation QA (Klaus, MaestroQA, Loris, Zendesk QA) and resolution-aware grading (Lorikeet Coach, with Forethought and Decagon grading inside their own stacks). The questions below force the distinction into the open.
Can you fail a ticket that read perfectly but left the action incomplete, and show me the rule that does it?
What percentage of tickets do you grade - the real number, not "AI-powered"?
Can you grade the resolutions produced by an AI agent independently, or does the agent grade itself?
When a ticket fails, do you cluster the cause across volume, or just flag the one ticket?
Can the grade reference the specific SOP step that passed or failed, for an auditable record?
Can I run you over my existing stack without replacing my helpdesk or resolution agent?
Lorikeet's Take on Resolution Grading
Most QA tools were built to coach humans, so they grade the conversation. That made sense when a person wrote every reply. It makes less sense when an AI agent resolves the majority of tickets, because the question is no longer "was the rep polite" but "did the resolution happen, and can we prove it followed the process." A friendly message that left the refund unprocessed is a failed ticket, and a tool that scores it well is measuring the wrong thing.
The pattern we believe in is grading the outcome and grading the AI with an independent AI. Coach scores 100% of tickets, verifies the resolution, ties failures to a root cause, and does it on top of whatever stack you already run. If the bar your team uses is "prove the resolution happened and the process held," see how Coach grades resolutions.
Key Takeaways
Resolution grading is different from conversation scoring: it verifies that the issue was solved and the SOP was followed, not that the reply was polite.
AI QA earns its keep through 100% coverage; manual review samples 1-3% and misses the costly long-tail failures.
As AI agents handle more volume, the urgent need is grading the AI itself - an independent "AI evaluating the AI" check - which is Lorikeet Coach's defining capability.
Most tools (Klaus, MaestroQA, Loris, Zendesk QA) grade conversations well; Forethought and Decagon grade inside their own resolution stacks.
Lorikeet Coach grades standalone at roughly $0.10 per ticket over an existing stack, with resolution verification and root-cause analysis for regulated teams.
Conclusion
The question for 2026 is not whether to automate QA - sampling 1-3% of tickets by hand was never enough, and it is hopeless once an AI agent is resolving most of your volume. The question is what the grade actually measures. A tool that scores tone and empathy will tell you your replies sound good. A tool that verifies resolutions will tell you whether your customers' problems were solved and whether you can defend how.
The seven tools above each fit a different need. Lorikeet Coach is the answer for teams that need every resolution verified, the AI agent itself graded, and an auditable reason each ticket passed or failed - especially in regulated industries where the failure mode is the only number that matters. The others are credible choices for conversation-quality programs or for grading inside a single vendor's stack.
If you are evaluating how to grade support resolutions at scale, see how Lorikeet Coach runs 100% automated QA over your existing stack.
