The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
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303 articles · Page 17 of 26
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.
Fine-Tune a 7B Model for $1,500 (Not $50,000)
Full fine-tuning costs $50K in H100s. QLoRA on an RTX 4090 costs $1,500. Learn how LoRA and QLoRA let you train only 0.1-1% of parameters with nearly identical results, with working code for fine-tuning models that understand your agent's tool schemas.
The AI Agent Dashboard of 2026: What Teams Actually Need to See
Traditional dashboards tell you what went wrong yesterday. The AI agent dashboards teams actually need deliver feedback in the moment, during the call, not after it. Here's what that looks like in practice.
The Three Protocols Every AI Agent Will Speak
The AI agent protocol stack has three layers: MCP for tools, A2A for agent-to-agent communication, and WebMCP for browser interaction. A practitioner's guide to how they work together in production.
A 1B Model Just Matched the 70B. Here's How.
How to distill frontier LLMs into small, cheap models that retain 98% accuracy on agent tasks. The teacher-student pattern, NVIDIA's data flywheel, and the Plan-and-Execute architecture that cuts agent costs by 90%.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
