Articles tagged “monitoring”
20 articles
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.
The no-code ceiling: when agent builders hit production
Visual agent builders get you to 80% fast. The last 20%, telephony, monitoring, testing, and memory, requires infrastructure they never intended to provide.
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 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.
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 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.
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 Has No Guardrails
Air Canada honored a refund its chatbot hallucinated. DPD's bot cursed at customers on camera. One e-commerce agent approved $2.3M in unauthorized refunds at 2:47 AM. Here is the five-layer guardrail architecture that prevents all three.
AI Agents Are Great. Until They're Not. When to Put Humans Back in Control
AI agents can handle 80% of your customer interactions with no problem. The other 20% is where your reputation is made or broken. Here's how to design escalation that actually works.
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.
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.
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.
What HIPAA Taught Us About AI Security (And It Applies to Every Industry)
Healthcare didn't choose to build the most rigorous data security framework in existence. It was forced to. Three decades later, that framework turns out to be the best blueprint for securing AI agents in any industry.
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.
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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