Articles tagged “scorecards”
34 articles
AI Agent KPIs: What to Measure Before You Ship
Only 31% of teams have a measurement framework for their AI agents. Here's how to define task completion rate, escalation rate, cost per outcome, and CSAT delta before your first production interaction.
Past 50 tools, function-calling accuracy falls off a cliff
Past 50 tools, function-calling accuracy falls off a cliff. Measure the curve on your own agent and recover accuracy with per-turn toolset scoping.
GPT-5, Claude 4.5, Gemini Score the Same Calls. Their Kappa Is 0.52
Run the same calls through GPT-5, Claude 4.5, and Gemini and Cohen's kappa lands at 0.52. Here is how to measure judge agreement on your own corpus.
How to Eval Agents When There's No Right Answer
Most eval methods assume you know the correct response. CX agents rarely have one. Here's how to score agent quality with criteria-based rubrics and LLM-as-judge, no labeled ground truth required.
How to Build a Healthcare Appointment Voice Agent (FHIR, 270/271, HIPAA)
Most voice AI tutorials stop after hello. The real build: identity verification, FHIR slots, 270/271 eligibility, A2P SMS, escalation, with HIPAA gates intact.
Building an AI Nurse Line Without Practicing Medicine
Health systems pay $20-30 per nurse-line call. AI is the obvious cost play, but every triage agent raises a malpractice question. Here's the safe architecture.
Build a Pharmacy Refill Voice Agent (NCPDP, DEA, 60-Second Refill)
Build a voice AI for prescription refills that respects DEA Schedule II, handles NCPDP refill-too-soon rejections, and routes the right calls to humans.
Is AI Better Than Your Humans? Score Both on One Rubric
Most teams can't say whether AI beats humans because they score them differently. One rubric, run on both, sliced by segment, gives you an honest answer.
Your LLM-as-judge may be highly biased
LLM-as-Judge has 12 documented biases. Here are 6 evaluation methods production teams actually use instead, with code examples and patterns.
Is monitoring your AI agent actually enough?
Research shows 83% of agent teams track capability metrics but only 30% evaluate real outcomes. Here's how to close the gap with multi-turn scenario testing.
Online vs. Offline Evals: Close the Production Gap
89% of teams have observability but only 37% run online evals. Here's why that gap is where production failures hide, and how to close it with a practical online eval pipeline.
LLM-as-a-Judge: Build a Production Eval Pipeline
Build a production LLM-as-a-judge eval pipeline step by step. Covers judge selection, rubric design, CI integration, and sampling strategies that scale.
We open-sourced our AI agent testing engine
chanl-eval is an open-source engine for stress-testing AI agents with simulated conversations, adaptive personas, and per-criteria scorecards. MIT licensed.
Build an AI Agent Observability Pipeline from Scratch
Build a production observability pipeline for AI agents using TypeScript and the Chanl SDK. Covers metrics, traces, quality scoring, drift detection, and alerting.
Production Agent Evals: Catch Score Drift, Ship Confidently
Your evals pass in staging but miss production failures. Build three eval pipelines with the Chanl SDK: automated scorecards, scenario regression, and drift detection that catches quality degradation before customers do.
Agent Drift: Why Your AI Gets Worse the Longer It Runs
AI agents silently degrade over long conversations. Research quantifies three types of drift and shows why point-in-time evals miss them entirely.
Banks Trust AI With Transactions. Why Not Customer Calls?
How a mid-size bank deploys AI agents for customer service with identity verification, PCI compliance, fraud detection, and regulatory scorecards.
Your Call Center Handles 10,000 Calls a Day. Who's Grading Them?
AI agents handle 40% of your calls. Your QA team samples 2%. The monitoring gap between deployment and quality is where enterprise reputations break.
The Shopping Assistant That Outsells Your Best Sales Rep
How a $50M fashion retailer turned 15,000 SKUs and customer purchase history into an AI shopping assistant that outsells human sales reps.
The Insurance Agent That Never Misquotes a Policy
How regional insurers deploy AI agents that answer policy questions accurately, intake claims end-to-end, and produce the audit trail regulators demand.
12 Ways Your LLM Judge Is Lying to You
Research identifies 12 systematic biases in LLM-as-a-judge systems. Learn to detect and mitigate each one before they corrupt your eval pipeline.
Your Agent Completed the Task. It Also Forgot 87% of What It Knew.
Task completion hides a silent failure: agents forget 87% of stored knowledge under complexity. New research reveals why standard evals miss this entirely.
74% of Production Agents Still Rely on Human Evaluation
A survey of 306 practitioners reveals most production agents are far simpler than expected. The eval gap isn't a tooling problem. It's a trust problem.
Your Agent Is Getting Smarter. It's Not Getting More Reliable.
Reliability improves at half the rate of accuracy. Three 85%+ tools combine to just 74%. Here's the math, the research, and the testing protocols that close the gap.
Your AI Assistant Works in Demo. Then What?
Test your AI shopping assistant with AI personas that simulate real customer segments, score conversations with objective scorecards, and monitor production metrics that matter for ecommerce.
Your Agent Aced the Benchmark. Production Disagreed.
We scored 92% on GAIA. Production CSAT: 64%. Here's which AI agent benchmarks actually predict deployed performance, why most don't, and what to measure instead.
AI Agent Testing: How to Evaluate Agents Before They Talk to Customers
A practical guide to testing AI agents before production — scenario-based testing with AI personas, scorecard evaluation, regression suites, edge case generation, and CI/CD integration.
Who's Testing Your AI Agent Before It Talks to Customers?
Traditional QA validates deterministic code. AI agent QA must validate probabilistic conversations. Here's why that gap is breaking production deployments.
From Analytics to Action: Turning Conversation Data Into Agent Improvements
Most teams collect call data and never use it. Learn how to close the loop from analytics to insight to prompt change to scorecard validation — and actually improve your AI agents.
Gartner Says 80% Autonomous by 2029. Here's What Nobody's Talking About.
Gartner predicts 80% autonomous customer service by 2029. But the gap between today's AI agents and that future requires testing, monitoring, and quality infrastructure most teams don't have.
Scorecards vs. Vibes: How to Actually Measure AI Agent Quality
Most teams 'feel' their AI agent is good. Here's how to build structured scoring with rubrics, automated grading, and regression detection that holds up.
Voice AI Testing Strategies That Actually Work: A Complete Framework for Production Success
Discover the comprehensive testing framework used by top voice AI teams to achieve 95%+ accuracy rates and prevent costly production failures. Includes real case studies and actionable implementation guides.
Automated QA Grading: Are AI Models Better Call Scorers Than Humans?
Industry research shows that 75-80% of enterprises are implementing AI-powered QA grading systems. Discover whether AI models actually outperform human call scorers and how to implement effective automated grading.
The Voice AI Quality Crisis: Why Most Deployments Fail in Production
Most voice AI deployments fail in production despite passing lab tests. Real data on why the gap exists, what it costs, and how to close it.
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