How to Build AI Agents

AI agents are software systems that use a language model as a reasoning engine to autonomously plan, take actions, use tools, and complete multi-step tasks without requiring human input at each step. Unlike a standard LLM API call that returns a single text response, an AI agent operates in a loop: it reads its current context, reasons about the next best action, calls a tool (web search, code execution, API call, file write), observes the result, and decides whether to continue or stop. This perception-reasoning-action loop is what makes agents capable of completing goals that require multiple decisions. A single agent can browse the web, read and write files, call external APIs, manage memory across sessions, and delegate subtasks to other agents — making them fundamentally more capable than any single LLM interaction for complex real-world tasks.

Building production-grade AI agents requires understanding the core architecture: a reasoning loop (often called an agent loop), tool definitions, memory systems, and error handling. The most common mistakes AI builders make are designing agents that are too broad in scope, lack proper observability, and have no fallback when the LLM makes wrong decisions.

At The AI How, we build AI agents with real-world constraints in mind. We cover LangGraph for stateful agent orchestration, the MCP protocol for tool standardization, memory patterns for long-running agents, and deployment patterns that make agents reliable in production.