#howtobuildaiagents

I show that an AI agent is essentially one loop that keeps calling the model while stop_reason is tool_use, plus a message list and your own Python

I build a tool-using AI agent from scratch with a free local model through Ollama, using a simple loop with a step cap and tool errors fed back to the

I walk through the stack I use to ship AI agents that run unattended: LangGraph for durable workflows, the Claude Agent SDK for open-ended tasks

I break down what agentic AI courses like Andrew Ng's and Hugging Face's teach well, and the gaps I only learned in production. My self-study path puts

I build first AI agents as narrow, controlled tool-use loops: the model proposes actions while my code enforces permissions, state, timeouts, logs, and

Most "AI consultants" ship demos, not systems that survive a 3 a.m. incident with retries, evals, cost caps, and observability. I help founders and teams

I ship AI agents to production weekly and pick from three stacks: a ~150-line DIY loop for simple linear tools, LangGraph when I need typed state

I run Claude Code as the execution layer for autonomous systems like BizFlowAI ContentStudio, and reliability came from workflow design, not smarter

I strip frameworks like LangChain and LangGraph out of production because an agent is really just a while-loop around an LLM call with tools, a hard step

I build unattended systems that replace recurring manual work—mapping the real process, automating deterministic parts with code and judgment calls with