The Chanl Blog
Insights on building, connecting, and monitoring AI agents for customer experience — from the teams shipping them.
All Articles
157 articles · Page 2 of 14
Claude Code subagents and the orchestrator pattern
How to structure Claude Code subagents, write dispatch prompts, and coordinate parallel work across services, SDKs, and frontends in a monorepo.
Graph memory for AI agents: when vector search isn't enough
Build graph memory for AI agents in TypeScript and Python. Extract entities, track relationships over time, and compare Mem0, Zep, and Letta in production.
Voice AI pipeline: STT, LLM, TTS and the 300ms budget
Build a real-time voice pipeline with Pipecat. How STT, LLM, and TTS stream concurrently under a 300ms latency budget, with turn detection and interruptions.
AI Agent Frameworks Compared: Which Ones Ship?
An honest comparison of 9 AI agent frameworks (LangGraph, CrewAI, Vercel AI SDK, Mastra, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Pydantic AI, AutoGen) based on what developers actually ship to production in 2026.
Build an AI Agent Observability Pipeline from Scratch
Build a production observability pipeline for AI agents using TypeScript and the Chanl SDK. Covers metrics, traces, quality scoring, drift detection, and alerting.
Your AI Agent's Context Window Is Already Half Full
System prompts, tool schemas, MCP descriptions, memory injection, conversation history. They all eat tokens before the user says a word. Learn where your context budget goes and how to manage it.
MCP vs A2A: Tools Protocol, Agents Protocol, and Why You Need Both
MCP connects agents to tools. A2A connects agents to each other. Most developers confuse them. This guide breaks down both protocols with architecture diagrams, real code, and a decision framework for production systems.
Production Agent Evals: Catch Score Drift, Ship Confidently
Your evals pass in staging but miss production failures. Build three eval pipelines with the Chanl SDK: automated scorecards, scenario regression, and drift detection that catches quality degradation before customers do.
Agent Drift: Why Your AI Gets Worse the Longer It Runs
AI agents silently degrade over long conversations. Research quantifies three types of drift and shows why point-in-time evals miss them entirely.
How to enforce the orchestrator pattern in Claude Code
The main Claude Code thread plans and reviews. Subagents implement. Three enforcement layers make this mandatory: CLAUDE.md, skills, and hooks. Includes a starter kit you can copy.
Banks Trust AI With Transactions. Why Not Customer Calls?
How a mid-size bank deploys AI agents for customer service with identity verification, PCI compliance, fraud detection, and regulatory scorecards.
Your Call Center Handles 10,000 Calls a Day. Who's Grading Them?
AI agents handle 40% of your calls. Your QA team samples 2%. The monitoring gap between deployment and quality is where enterprise reputations break.
Learn Agentic AI
One lesson a week — practical techniques for building, testing, and shipping AI agents. From prompt engineering to production monitoring. Learn by doing.
