Articles tagged “analytics”
27 articles
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.
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.
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.
What to Trace When Your AI Agent Hits Production
OpenTelemetry GenAI conventions are the production standard for agent tracing. What to instrument, what to skip, and what breaks — from a 2 AM debugging war story.
The AI Agent Dashboard of 2026: What Teams Actually Need to See
Traditional dashboards tell you what went wrong yesterday. The AI agent dashboards teams actually need deliver feedback in the moment, during the call, not after it. Here's what that looks like in practice.
Stop Reacting to Bad Calls. Catch Problems Before Customers Do
By the time a customer complains, you've already lost. Real-time analytics lets AI agent teams catch failing conversations mid-flight, not in the post-mortem. Here's how to build a proactive monitoring stack that prevents pain instead of documenting it.
Your AI Agent Costs $13K/Month. Here's the Fix.
A production customer-service agent burned $13,247 in one month. Prompt caching, model routing, batch processing, and plan-and-execute architecture cut it to $1,100. Real pricing math for every technique.
Your AI Agent Isn't Learning From Production. Here's What That's Costing You.
Most AI agents are deployed and forgotten. The teams winning with AI have a different strategy: closing the loop from every live call back into the agent itself.
AI Agent Observability: What to Monitor When Your Agent Goes Live
Build a production observability pipeline for AI agents. Covers latency, token usage, tool success rates, conversation quality, drift detection, structured logging, alerting strategies, and the critical difference between LLM and agent observability.
Call Logs Aren't Just Records. They're Your Best Product Feedback Loop
Most teams treat call logs as a compliance archive. The teams winning with AI agents treat them as a real-time signal about what's working, what's breaking, and what customers actually want.
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.
Real-Time Monitoring for AI Agents: What to Watch and When to Panic
What dashboards actually matter for production AI agents. Alert fatigue, anomaly detection, and the metrics that predict failures before customers notice.
How AI Agent Interactions Create Better Human Agents: The Feedback Loop Nobody Talks About
Every AI agent interaction generates training data that can improve human agent performance. Here's how the feedback loop between AI and human learning actually works in production contact centers.
Voice AI Can Read Your Mood — Here's What That Changes
How emotion-aware voice AI detects customer sentiment in real time, adapts responses, and cuts escalations by 25-40% — plus the ethics you can't ignore.
Voice Commerce Hit $50B. Here's How Amazon, Google, and Apple Are Splitting It
Analyze the explosive growth of voice commerce and how Amazon, Google, and Apple are competing to dominate voice-activated shopping experiences.
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.
Conversational Analytics Gone Wrong: Top Pitfalls in Call Data Interpretation
Industry research shows that 70-75% of enterprises misinterpret conversational AI analytics, leading to costly business decisions. Discover the most common pitfalls and how to avoid them.
Silent Monitoring by AI: Quality Assurance Without Human Eavesdropping
Industry research shows that 70-75% of enterprises are implementing AI-powered silent monitoring for quality assurance. Discover how automated QA transforms agent performance without privacy concerns.
Moving Past "Average Handle Time": New Metrics for Evaluating Conversational AI
Industry research shows that 60-65% of enterprises still rely on Average Handle Time, missing critical conversational AI metrics. Discover the next-generation metrics that drive real business value.
Performance Benchmarks for AI Agents: What Actually Matters Beyond Word Error Rate
Most enterprises obsess over Word Error Rate while missing the metrics that actually predict success. Here's what to measure instead.
What Voice AI Can (and Can't) Learn from Your Best Human Agents
Top human agents do specific things that make them exceptional. Some of those things can be taught to AI. Others can't, at least not yet. Here's an honest breakdown of what transfers and what doesn't.
Mining the Conversation Long Tail: How Production Data Reveals What Humans Miss
Most teams analyze their top 20 conversation types and ignore the rest. The long tail of rare customer requests holds the patterns that drive real agent improvement. Here is how to mine it.
Voice AI as the New Front Door: Rethinking Customer Journey Mapping for Conversational Interfaces
Industry research shows 60-65% of enterprises are redesigning customer journeys around voice AI as the primary touchpoint. Discover how conversational interfaces are reshaping customer experience design.
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.
The 16% Rule: How Every Second of Latency Destroys Voice AI Customer Satisfaction
Research shows each second of latency reduces customer satisfaction by 16%. Learn the technical causes of voice AI delays and discover testing strategies to maintain sub-second response times.
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One lesson a week — practical techniques for building, testing, and shipping AI agents. From prompt engineering to production monitoring. Learn by doing.
