Learning AI Articles
34 articles · Page 1 of 3
Context engineering for reliable CX agents
Context engineering is the discipline of deciding what information your AI agent sees, when it sees it, and how it's formatted. Here's how to apply it to production CX systems.
Context engineering: why your agent gets dumber mid-conversation
82% of AI teams say prompt engineering alone no longer works for production. Context engineering decides what your agent sees, when, and what gets removed as conversations grow.
Correlation Killed Your Retention Model. Causal AI Fixes It.
Your churn model says support calls cause retention. They don't. Build a causal pipeline with DoWhy, EconML, and propensity matching in Python.
Build the MCP + A2A agent protocol stack from scratch
Wire an MCP server to an A2A agent that delegates tasks and calls tools. TypeScript and Python examples, Streamable HTTP transport, Agent Cards, and auth.
Agentic RAG: from dumb retrieval to self-correcting agents
Your RAG pipeline retrieves wrong documents and nobody catches it. Build a self-correcting agent that grades results, rewrites queries, and knows when to stop.
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.
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.
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.
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.
El briefing de Signal
Un email por semana. Cómo los equipos líderes de CS, ingresos e IA están convirtiendo conversaciones en decisiones. Benchmarks, playbooks y lo que funciona en producción.
