The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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157 articles · Page 10 of 14
Prompt Engineering Is Dead. Long Live Prompt Management.
Why production AI teams need version control, A/B testing, and rollback for prompts — not just clever writing. The craft has changed.
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.
Scenario Testing: The QA Strategy That Catches What Unit Tests Miss
Discover how synthetic test conversations catch edge cases that unit tests miss. Personas, adversarial scenarios, and regression testing for AI agents.
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.
The Tool Explosion: Managing 50+ Agent Tools Without Losing Your Mind
As agents get more capable, tool sprawl becomes a real operational problem. Here's how to organize, test, and monitor function calling at scale before it breaks in production.
AI Agent Memory: Build Your Own or Buy Off the Shelf?
Comparing Mem0, Zep, Letta, and custom memory for AI agents. We break down architecture trade-offs, compliance risks, and when each approach makes sense.
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.
Edge AI for Voice Agents: Fix Latency and Privacy at the Source
How edge AI eliminates 50-200ms of latency and entire classes of privacy risks for voice agents — with hybrid architecture patterns and TypeScript examples.
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.
The Multilingual Voice AI Challenge: Breaking Language Barriers While Maintaining Quality
Explore the technical complexities of multilingual voice AI including accent adaptation, cultural context, and quality assurance across languages.
How Multimodal Voice AI Works: From Audio-Only to Vision-Aware Agents
How multimodal voice AI combines speech, vision, and text into a single agent — architecture patterns, latency tradeoffs, and TypeScript code you can run.
Sub-300ms Voice AI: The New Standard That's Redefining Customer Expectations
Discover why sub-300ms response times have become the new standard in voice AI, backed by cognitive science research and real-world deployment data.
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