Practical Guide to Building AI Agents with Tool Use: Patterns That Actually Work in Production

Every week there's a new "autonomous AI agent" framework on GitHub with 10k stars and a demo that books flights, writes code, and orders pizza. Every week, teams try to use these in production and discover they hallucinate tool calls, burn through API budgets in minutes, and get stuck in infinite loops. The gap between agent demos and production agents is enormous. This guide bridges it. We'll build a minimal agent framework from scratch, implement battle-tested patterns for tool use, and be honest about when you should skip agents entirely. No frameworks, no magic -- just Python, an LLM API, and hard-won lessons from shipping agents that handle real workloads. What AI Agents with Tool Use Actually Are Strip away the hype and an AI agent is just a loop: The LLM receives a task and a list of available tools It decides which tool to call (or whether to respond directly) The tool executes and returns a result The LLM sees the result and decides what to do next That's it. The "intelligence