QEval® audits AI agents in production under the VERDICT Method, the independent audit standard for the AI workforce. Pick a voice, chat, or email conversation below and read the full audit: what the agent did, whether it was good, and every issue with the fix. Free with a work email.
The most documented AI agent incidents share one root cause: the metrics were the vendor's own, and nobody independently audited the conversations until a customer, a court, or a union did.
The fix in every working deployment is the same: an independent measurement loop that the agent's builder does not control. Ours has a name.
QEval®'s independent audit standard for AI agents. Seven commitments, each one auditable, applied to production conversations on the same scorecard your human team is held to. Vendor benchmarks test agents before launch. VERDICT audits them after, conversation by conversation.
Start from what the agent actually did: latency, turns, tool calls, escalations, containment. Telemetry is the input to the audit, never the grade. A green trace can sit on top of a conversation that broke a disclosure rule.
Every dimension score traces to the exact transcript turn that earned it, so a supervisor or compliance officer can see the moment instead of trusting a summary. No black-box verdicts.
A contact closed without a human is not a contact resolved. The audit scores whether the issue was actually solved, correctly and compliantly, and treats repeat contact as the truth serum. Containment is coverage, not quality.
AI identity, recording consent, payment disclosures, and channel law are checked as hard gates, not style points. The rules are now statute: the TCPA covers AI voices, state laws carry private rights of action, and the EU AI Act requires disclosure from August 2026.
Every failure maps to a named failure family, from disclosure miss to hallucination to context loss, with a confidence score and a recurrence count. One bad conversation finds every conversation like it.
Deterministic and classifier checks run on 100% of interactions. Model-judge scoring is sampled and validated against human supervisors, and recalibrated whenever the two diverge. Accuracy is contractual: 94%+, written into the master agreement.
Every issue ships with the fix: the knowledge-base or guardrail action to apply, with a projected lift. Every applied fix is re-scored to confirm the lift held. An audit without action is just a report.
Why it has to be independent. The vendor that built your AI agent controls both the design and the disclosure of its own evaluations. QEval® is built by an operator that runs 4,000+ human agents and scores its own floor on the same standard, and it has no AI agent to sell you. The scorecard below runs the method in front of you.
Six real-pattern conversations across voice, chat, and email, each audited under the VERDICT Method. The seven stages run in order: verified telemetry, evidence-pinned scores, the resolution call, compliance gates, the issue ledger, calibration, and the projected lift if you apply the fixes. Add a work email to lift the glass.
Open the audit room. What you can see under the glass is the live scorecard: a graded voice, chat, or email AI conversation with evidence-pinned scores, compliance gates, and every issue with the fix. Add your work email and the glass lifts.
Your scoring stays in your browser. We will email your scorecard and the VERDICT audit checklist. No spam, unsubscribe anytime.
Voice is where AI agents are scaling fastest and failing hardest. Production latency, interruptions, accents, and background noise break agents that looked perfect in the demo, and the disclosure rules are now statute, not etiquette. QEval® scores 1M+ voice AI calls every day, so the audit reflects production, not a lab.
AI observability tools show what the agent did: latency, tokens, tool calls, traces, and the containment rate. None of that says whether the conversation was good for the customer or safe for the business. The VERDICT Method starts where the trace ends.
Containment is not resolution. An AI agent that contains a contact can still leave the customer unresolved, and a green trace can sit on top of a conversation that broke a disclosure rule. The telemetry is the input; the verdict is the judgment.
Most automated QA fails on trust: scores that do not match the company's own scorecard, and no way to check the machine. The VERDICT Method is built to be inspected.
Disclosure, consent, and policy gates run as deterministic and classifier checks on every interaction, not a sample.
Model-judge scoring on qualitative dimensions is sampled and validated against human supervisors, the way a QA team calibrates its reviewers.
When human and machine judgments drift apart, the expert model is recalibrated before its scores count. Disagreement is a signal, not a footnote.
Every classification traces to the expert sub-model that produced it and the transcript span that triggered it. You can always ask the system to show its work.
The same scorecard as your humans. Scoring runs on a proprietary multi-expert model with a purpose-trained expert for each dimension, at a contractual 94%+ accuracy. AI agents are graded on the rubric your human team is held to, so "good enough for the bot" stops being a separate, lower bar.
Quality and compliance is the first of QEval®'s Six Layers of Intelligence. The same audited conversation also feeds customer, operational, and training intelligence, which is where most of the value is.
Whether you evaluate QEval® or anyone else, these questions separate production platforms from demonstrations.
The VERDICT Method is QEval's independent audit standard for AI agents. Seven commitments: Verify the telemetry, Evidence-pin every score to the exact transcript turn, score Resolution over containment, check Disclosure and compliance as hard gates, map every failure to a named Issue taxonomy, Calibrate machine scoring against human supervisors, and Track every fix by re-scoring to confirm the lift. It runs on production conversations, on the same scorecard as human agents, backed by a contractual 94%+ accuracy SLA.
An AI agent scorecard is a structured quality report that audits a voice, chat, or email bot conversation. It separates observability telemetry (what the agent did) from a quality score across resolution, containment quality, groundedness, compliance, empathy and CSAT, brand voice, and action accuracy, then lists each issue with what went wrong, why, where in the transcript, and the recommended fix.
Containment measures the absence of a human handoff, not whether the customer's issue was solved. A bot can report high containment while the customer abandoned, got a wrong answer, or contacts again the next day. The VERDICT Method scores resolution and repeat contact, and treats containment as a coverage metric, not a quality metric.
Voice adds timing and speech metrics (word error rate, turn latency, barge-in recovery) and hard legal gates at connection: recording consent and AI-identity disclosure under the TCPA and state law. Chat centers on retrieval groundedness, citation accuracy, and the deflection versus resolution gap. Email centers on full-thread comprehension, answer completeness, routing accuracy, and auto-reply-loop safety under RFC 3834.
Deterministic and classifier checks run on 100% of interactions, while model-judge scoring is sampled and validated against human reviewers, with recalibration when human and model judgments diverge. Scoring runs on a proprietary multi-expert model backed by a 94%+ accuracy SLA, and every classification traces to the expert and the transcript span that triggered it.
Bring your scorecards, your AI agents, your CCaaS. We will audit a real conversation in 30 minutes under the VERDICT Method and show you what your current program missed last week.