Articles tagged “evaluations”
12 articles
How to Measure Cost Per Successful Outcome for AI Agents
Most teams measure AI agent quality by pass rate. The metric that actually predicts ROI is cost per successful outcome: what each resolution costs paired against whether it actually resolved. Here's how to build it.
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.
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.
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.
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.
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 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 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.
Como evaluar agentes de IA: construye un framework de evaluacion desde cero
Construye un framework funcional de evaluacion de agentes de IA en TypeScript y Python. Cubre LLM-as-judge, puntuacion por rubrica, pruebas de regresion e integracion con CI.
The Signal Briefing
Un email por semana. Cómo los equipos líderes de CS, ingresos e IA están convirtiendo conversaciones en decisiones. Benchmarks, playbooks y lo que funciona en producción.
