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 7 of 14
Your Agent Passed Every Dev Test. Here's Why It'll Fail in Production
A 4-layer testing framework for AI agents (unit, integration, performance, and chaos testing) so your agent survives real customers, not just controlled demos.
MCP Is Now the Industry Standard for AI Agent Integrations. Here's What That Means
MCP standardizes how AI agents connect to tools and data, replacing fragile, proprietary integrations with a universal protocol. Here's what it means for your agents.
Claude 4.6 broke our production agent in two hours — here's what's worth the migration
A practical developer guide to Claude 4.6 — adaptive thinking, 1M context, compaction API, tool search, and structured outputs. Real code examples in TypeScript and Python for building production AI agents.
Your Voice Agent Forgets Everything. Here's How to Fix That
How to add persistent memory, tools, and knowledge to Pipecat and LiveKit voice agents using the Chanl Python SDK — one SDK instead of assembling five services.
71% of organizations aren't prepared to secure their AI agents' tools
MCP gives AI agents autonomous access to real systems — and introduces attack vectors that traditional security can't see. A technical breakdown of tool poisoning, rug pulls, cross-server shadowing, and the defense framework production teams need now.
MCP Streamable HTTP: The Transport Layer That Makes AI Agents Production-Ready
MCP's Streamable HTTP transport replaced the original SSE transport to fix critical production gaps. This guide covers what changed, why it matters, and how to implement it in TypeScript with code examples.
Conversational AI vs. Agentic AI: What's the Difference, and Why It Matters for CX Teams
Conversational AI follows scripts. Agentic AI pursues goals. Here's the exact difference, with a side-by-side comparison and a practical guide to choosing the right approach for customer experience.
Your agent has 30 tools and no idea when to use them
MCP tools give agents external capabilities. Skills give agents behavioral expertise. Learn the architecture of both, build them in TypeScript, and understand when to use each — and when you need both.
The Death of the Decision Tree: Why Rule-Based Bots Can't Survive Real Conversations
Scripted voicebots break the moment customers go off-script, which is most of the time. Here's exactly how decision trees fail, what agentic AI changes at the architecture level, and how to make the transition without a catastrophic cutover.
Is Your AI Agent Actually Ready for Production? The 3 Tests Most Teams Skip
Most AI agent failures happen not because the agent is bad, but because it was never properly tested. Here's the testing framework (unit, A/B, and live) that catches what demos miss.
Agentic AI in Production: From Prototype to Reliable Service
Ship agentic AI that doesn't break at 2 AM. Covers orchestration patterns (ReAct, planning loops), error handling, circuit breakers, graceful degradation, observability, and scaling — with TypeScript implementations you can steal.
AI Agent Memory: From Session Context to Long-Term Knowledge
Build AI agent memory systems from scratch in TypeScript. Covers memory types (session, episodic, semantic, procedural), architectures (buffer, summary, vector retrieval), RAG intersection, and privacy-first design.
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One lesson a week — practical techniques for building, testing, and shipping AI agents. From prompt engineering to production monitoring. Learn by doing.
