How To Use AI

"Do I need to learn Python to build my own AI agent?" I pause every time I hear that question. Two years ago I would have said "you probably need at least the basics." Today my answer is different. Bottom line: Building an AI agent does not require code. Defining an agent's role, goal, constraints, tools, and output format in a Markdown document — that act itself is building an agent. Platforms like Claude Projects, ChatGPT Custom GPTs, and Dify already provide that environment. One system prompt replaces hundreds of lines of Python. I have spent 25 years as a network engineer and written more Ansible playbooks than I can count. Deploying VLANs to routers was done in YAML, not Python. A declarative instruction — "apply this config to this interface" — drove complex automation. An AI agent's system prompt feels exactly like that. You define behavior ...

An AI agent is a software system that receives a goal, plans the steps to achieve it, calls external tools, and iterates until the task is done — all without waiting for your next prompt. Applying an agent loop to GPT-3.5 raised its accuracy from 48.1% to 95.1%, a bigger jump than upgrading to GPT-4. ▶ Table of Contents (click to expand) AI Agent vs. Chatbot — What Actually Changes? The Five Components — But One Outweighs the Others The ReAct Loop — How an AI Agent Actually Thinks ChemCrow — The Case That Surprised Me Most The Honest Part — Where Agents Still Fail The Thing That Stuck With Me An AI agent is a software system that receives a goal, plans the steps to achieve it, calls external tools, and iterates until the task is done — all without wai...

The main types of AI agents are reactive, goal-based, utility-based, learning, and autonomous. Each type is defined by whether it has memory, can plan, and can learn from experience. Matching the right type to the complexity of your problem is the core design decision. ▶ Table of Contents (click to expand) How to Classify the Types of AI Agents Reactive Agents — Fast, Stateless, and Surprisingly Useful Goal-Based and Utility-Based Agents — Planning and Trade-Offs Learning Agents — The Most Misunderstood Type of AI Agent Autonomous Agents — Goal In, Everything Else Handled Choosing the Right Type of AI Agent — Start Simple Final Thought Someone on your team says, "Let's add an AI ...

Bottom line: An AI agent works by running a continuous loop — perceive, plan, act, reflect — where each step calls real tools and writes results into growing memory. Unlike a chatbot that stops after one reply, the agent keeps looping until the goal is done or a stop condition fires. ▶ Table of Contents (click to expand) Three Lines of Code That Explain How AI Agents Work ReAct: The Reasoning Engine Inside Every AI Agent Tool Calling: How an AI Agent Touches the Outside World Memory: What an AI Agent Keeps, and What Gets Lost Multi-Agent Systems: Why Teams Beat Solo Agents One Last Thing Ask a chatbot to plan a cycling tour of Paris — you get a nicely written paragraph. Ask an AI agent the same thing, a...

As a network engineer who has been using both tools for a while now, I get this question a lot: Antigravity or Claude Code — which one should I use? Having tried both, my honest answer is: it depends on how you work. This post lays out the real differences between the two tools and helps you figure out which fits your situation — including a method I've been using myself: running Claude Code as an extension inside Antigravity. ▶ Table of Contents (click to expand) GUI IDE Comparison — The Fundamental Difference Between the Two Approaches Real-World Performance Differences — Strengths and Weaknesses Revealed in Testing Using Claude Code as an Extension — How to Use Claude Inside Antigravity's Chat Panel Choosing the Right Tool — Criteria by Situation Q&A — Frequently Asked Questions Conclusion GUI IDE Comparison ...