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The Chanl Blog

Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.

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215 articles · Page 9 of 18

Person building with tool components at a desk
Learning AI·20 min read

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.

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Person examining a branching diagram of document retrieval paths
Knowledge & Memory·12 min read

The RAG You Built Last Year Is Already Outdated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.

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Person examining documents through a magnifying glass
Knowledge & Memory·7 min read

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.

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Person connecting different shaped puzzle pieces together
Tools & MCP·7 min read

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.

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Office workers are busy working on computers. - Photo by TECNIC Bioprocess Solutions on Unsplash
Agent Architecture·14 min read

The Buffering Bug That Quietly Breaks Voice Agent Latency

SSE streams fine locally, then tokens batch into 500ms bursts in production. Here's why, how to fix it, and why pipeline parallelism matters more than model speed.

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Warm watercolor illustration of interconnected data streams flowing through a library-like space
Tools & MCP·13 min read

From Keyword Search to Shopping Memory

Build the intelligence layer for an AI shopping assistant: semantic product search with Commerce MCP, customer memory that persists across visits, and MCP tool registration for multi-channel deployment.

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Warm watercolor illustration of a control room monitoring shopping conversations
Tools & MCP·13 min read

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.

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Warm watercolor illustration of a workshop bench assembling colorful product cards
Tools & MCP·13 min read

Why AI Shopping Still Feels Like a Search Bar

Most AI shopping assistants return walls of text. Learn how ChatKit widgets and Vercel AI SDK structured output turn AI recommendations into interactive product cards with images, prices, and add-to-cart buttons.

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Man presenting charts to colleagues in a meeting. - Photo by Vitaly Gariev on Unsplash
Industry & Strategy·12 min read

Every Contact Center Job Is Changing. Here's What That Actually Looks Like

AI isn't eliminating contact center roles. It's hollowing out the repetitive parts and elevating the rest. Here's what human-AI collaboration actually looks like on the floor, and what it means for how you build and manage your team.

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Warm watercolor illustration of a woman at a sunlit flower market, holding a phone to her ear while browsing bouquets
Voice & Conversation·12 min read

Customers Don't Trust AI Voices. Here's What Actually Changes That

More than half of users instinctively distrust AI voices, not because the technology is broken, but because most deployments hide the wrong things and reveal nothing useful. Here's what transparency and UX actually do to close the gap.

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Illustration of an AI agent navigating branching knowledge paths across interconnected document nodes
Learning AI·18 min read

Your RAG Pipeline Is Answering the Wrong Question

Naive RAG scores 42% on multi-hop questions. Agentic RAG hits 94.5%. The difference: letting the agent decide what to retrieve, when, and whether the results are good enough. Build both in TypeScript and Python.

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Data visualization showing the gap between AI agent benchmark scores and production performance metrics
Testing & Evaluation·13 min read

Your Agent Aced the Benchmark. Production Disagreed.

We scored 92% on GAIA. Production CSAT: 64%. Here's which AI agent benchmarks actually predict deployed performance, why most don't, and what to measure instead.

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