Picture a warehouse stocked with every power tool ever made. Now picture the lights off, no labels, and no inventory list. That’s roughly where AI assistants are today. Modern AI clients like Claude, ChatGPT, Copilot, and Gemini aren’t limited to what the model knows anymore. They can plug into external tools, MCP servers, APIs, Skills, […]
Read More →Based on “5 things every AI engineer should know about agent sandboxes” by Ryan Ismert and Alan Blount of Google Cloud Tech (September 2026). Table of Contents Imagine hiring a brilliant intern who works at lightning speed, never sleeps, and occasionally does something completely unpredictable. Now imagine giving that intern the keys to your entire […]
Read More →Ask some reasoning models “What’s 2+3?” and they’ll chew through 900+ tokens before telling you “5.” That’s like hiring a consultant who writes a ten-page memo to confirm the sky is blue, then bills you by the page. This is the problem token-budget-aware reasoning solves. Here’s what it is, why it matters, and how to […]
Read More →Would you let a brand-new employee wire $50,000 on their first day without anyone checking? Probably not. You’d train them, watch their work, and hand over more responsibility as they earned it. AI deserves the same treatment. That’s the idea behind Human-in-the-Loop (HITL). Table of Contents What Is Human-in-the-Loop? Human-in-the-Loop is a way of building […]
Read More →A claim has been making the rounds in AI coding circles: stop prompting your coding agents and start designing loops that prompt them for you. Like most things, it gets repeated far more than it gets explained. This is the practical version — what an agent loop is, why it matters, and what one actually […]
Read More →