How to Test AI Agents for Production Failures Before Your Users Do
An AI agent that's flawless in the demo can still fall apart the first time a tool fails in production: a timeout, a network error, a response that comes back c
An AI agent that's flawless in the demo can still fall apart the first time a tool fails in production: a timeout, a network error, a response that comes back c
A static AI agent re-reasons the same kind of task from scratch every time, burning tokens and sometimes getting it wrong differently on each run. A self-improv
When an AI agent hallucinates a fact, the real damage starts when it writes that fact to memory and re-reads it as trusted context every session after, compound
On a multi-step task, an AI agent will trust a tool that reports success even when the work silently never saved, and then confidently report the whole task don
This week: I published my book on AI agents that don't hallucinate or waste tokens, Strands cut agent costs in half, GLM-5.2 open weights, and the agent launches from AWS Summit New York.
Un agente de IA tiene dos tipos de memoria: conversación (semántica) y contexto (referencia exacta). Mantenlas separadas con Strands y AgentCore.
An AI agent has two kinds of memory: conversation (semantic) and context (exact reference). Keep them separate with Strands and AgentCore.
Welcome to my very first newsletter!
Tu agente de programación con IA alucina APIs de AWS porque está adivinando con datos de...
AI coding agent hallucinates AWS APIs because it's guessing from training data frozen in the...