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 14 of 20
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.
AI Agent Observability: What to Monitor When Your Agent Goes Live
Build a production observability pipeline for AI agents. Covers latency, token usage, tool success rates, conversation quality, drift detection, structured logging, alerting strategies, and the critical difference between LLM and agent observability.
AI Agent Testing: How to Evaluate Agents Before They Talk to Customers
A practical guide to testing AI agents before production — scenario-based testing with AI personas, scorecard evaluation, regression suites, edge case generation, and CI/CD integration.
AI Agent Tools: MCP, OpenAPI, and Tool Management That Actually Scales
How production AI agents discover, execute, and manage tools — from MCP protocol to OpenAPI auto-importing, security sandboxing, and multi-tenant tool infrastructure.
Build your own AI agent memory system — what breaks when real users show up?
Build a complete memory system for customer-facing AI agents — session context, persistent recall, semantic search. Then learn what breaks when real customers start returning.
Build your own AI agent tool system — what breaks when you add the 20th tool?
Build a complete tool system for customer-facing AI agents from scratch — registry, execution, auth, monitoring. Then learn what breaks when real customers start calling.
Call Logs Aren't Just Records. They're Your Best Product Feedback Loop
Most teams treat call logs as a compliance archive. The teams winning with AI agents treat them as a real-time signal about what's working, what's breaking, and what customers actually want.
Learn Agentic AI
Weekly. Patterns for shipping agents that work — MCP, scorecards, regression tests, prompts, model comparisons.
