The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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235 articles · Page 11 of 20
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.
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.
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.
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.
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.
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.
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.
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.
Your Agent Remembers Everything Except What Matters
ICLR 2026 MemAgents research reveals when AI agents need episodic memory (what happened) vs semantic memory (what's true). Covers MAGMA, Mem0, AdaMem papers, comparison of Mem0 vs Letta vs Zep, and architecture patterns with TypeScript examples.
What to Trace When Your AI Agent Hits Production
OpenTelemetry GenAI conventions are the production standard for agent tracing. What to instrument, what to skip, and what breaks — from a 2 AM debugging war story.
Context Engineering Is What Your Agent Actually Needs
Prompt engineering hits a wall with production AI agents. Context engineering fixes it. Build a full context pipeline with memory, RAG, history compression, and tool resolution.
A 7B Domain Model Beat Everything We Tried
Domain-specific language models are beating trillion-parameter generalists on vertical tasks. Here's when a 7B model is the right call, how the training pipeline works, and what production teams are shipping today.
Learn Agentic AI
Weekly. Patterns for shipping agents that work — MCP, scorecards, regression tests, prompts, model comparisons.
