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 9 of 14
Who's Testing Your AI Agent Before It Talks to Customers?
Traditional QA validates deterministic code. AI agent QA must validate probabilistic conversations. Here's why that gap is breaking production deployments.
How to Evaluate AI Agents: Build an Eval Framework from Scratch
Build a working AI agent eval framework in TypeScript and Python. Covers LLM-as-judge, rubric scoring, regression testing, and CI integration.
MCP Explained: Build Your First MCP Server in TypeScript and Python
Build a working MCP server from scratch in TypeScript and Python. Hands-on tutorial covering tools, resources, transports, and testing.
Prompt Engineering from First Principles: 12 Techniques Every AI Developer Needs
Master 12 essential prompt engineering techniques with real TypeScript examples. From zero-shot to ReAct, build better AI agents from first principles.
RAG from Scratch: Build a Retrieval-Augmented Generation Pipeline
Build a working RAG pipeline from scratch in TypeScript and Python. Covers embeddings, chunking, vector search, and generation with real, runnable code.
Voice AI Escaped the Call Center. Here's Where It Landed.
From $50K M&A due diligence to 9 million burger orders, voice AI agents are breaking into verticals nobody predicted. Here's what developers need to know.
Your Voice AI Platform Is Only Half the Stack
VAPI, Retell, and Bland handle voice orchestration. Memory, testing, prompt versioning, and tool integration? That's all on you. Here's what to build next.
Your AI agent remembers everything — should your customers be worried?
Privacy-first memory design for AI agents: what to store, what to forget, how to give customers control, and how to stay compliant across GDPR, HIPAA, and multi-channel deployments.
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.
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.
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