The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
All Articles
195 articles · Page 13 of 17
The Knowledge Base Bottleneck: Why RAG Alone Isn't Enough for Production Agents
RAG works beautifully in demos. In production, stale data, chunking failures, and unscored retrieval quietly sink your AI agents. Here's what actually fixes it.
The MCP Marketplace Problem: Why Standardized Integrations Need Standardized Testing
5,800+ MCP servers, 43% with injection flaws. Standardized protocol doesn't mean standardized quality. Why every MCP integration needs automated testing.
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.
The Signal Briefing
One email a week. How leading CS, revenue, and AI teams are turning conversations into decisions. Benchmarks, playbooks, and what's working in production.
