AI Agents for Practical Applications

<p>Managing chronic conditions like Type 1 Diabetes is an exhausting, 24/7 mental load. Patients have to constantly monitor <strong>Continuous Glucose Monitor (CGM)</strong> data,

<p>A single AI agent with a few tools can handle simple tasks. But real-world workflows — process a customer refund, generate a research report, onboard a new user — involve

<h1> The Agent Loop </h1> <p><em>The entire AI agent stack in 60 lines of Python.</em></p> <p><a class="article-body-image-wrapper"

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>Most LangGraph content stops at the notebook. You build a cute ReAct loop, it answers one question, and the article ends before the hard part: <em>how do you actually serve

<p>When it comes to healthcare, "hallucination" isn't just a quirky AI bug—it's a critical safety risk. Building a system that flags <strong>Drug-Drug Interactions (DDI)</strong>