The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
All Articles
235 articles · Page 10 of 20
Your Agent Is Getting Smarter. It's Not Getting More Reliable.
Reliability improves at half the rate of accuracy. Three 85%+ tools combine to just 74%. Here's the math, the research, and the testing protocols that close the gap.
The Auto Shop That Knows Your Car Better Than You Do
Build an AI phone agent for auto repair shops that answers calls, quotes brake jobs, remembers every vehicle, and sends maintenance reminders.
A Dental Receptionist That Works Nights and Weekends
Build an AI receptionist for dental clinics that answers insurance questions, books appointments, and captures after-hours leads. Five clients pay $1,500/month.
The HVAC Company That Never Misses a Call
Build an AI receptionist that answers HVAC calls 24/7, triages emergencies, and books appointments. Then sell it as a service for $400-500/mo per client.
The Real Estate Agent Who Qualified Leads While Sleeping
Build an AI lead qualifier for real estate agents. Respond in under 60 seconds, score by budget and timeline, match listings, and book showings automatically.
Build a Restaurant AI That Remembers Every Regular
Build an AI phone agent for a local restaurant that takes orders, answers menu questions, and remembers regulars. A developer side hustle worth $400/month per client.
50 Tools, Zero Memory. The Biggest Gap in AI Agents Today
AI agents can call 50 APIs but can't remember what you said yesterday. The tool layer is years ahead of the memory layer, and customers are paying the price.
Embeddings Turn Text Into Meaning. Here's the Math and the Code
What embeddings are, how similarity search works under the hood, and how to build a semantic search engine, from cosine similarity math to production vector databases.
Function Calling: Build a Multi-Tool AI Agent from Scratch
Build a multi-tool AI agent from scratch using function calling across OpenAI, Anthropic, and Google. Runnable TypeScript and Python code, validation with Zod and Pydantic, and production hardening patterns.
The RAG You Built Last Year Is Already Outdated
RAG has branched into 5 distinct architectures: Self-RAG, Corrective RAG, Adaptive RAG, GraphRAG, and Agentic RAG. Here's when to use each and how to choose.
Your RAG Returns Wrong Answers. Upgrading the Model Won't Help
Most RAG quality problems are retrieval problems, not model problems. Bad chunking, wrong embeddings, and missing re-ranking cause more hallucinations than model capability gaps.
Why MCP Exists: Tool Calling Shouldn't Need Adapter Code
OpenAI, Anthropic, and Google all implement function calling differently. MCP is emerging as the standard that saves developers from writing adapter code for every provider.
Learn Agentic AI
Weekly. Patterns for shipping agents that work — MCP, scorecards, regression tests, prompts, model comparisons.
