5 AI Agent Patterns Every Developer Should Know in 2026

AI agents are moving from research demos to production systems. But most tutorials still show toy examples. Here are 5 battle-tested patterns that actually work at scale. 1. ReAct (Reasoning + Acting) The most versatile pattern. The agent thinks about what to do, takes an action, observes the result, and repeats. def run_react_agent(task, tools, max_steps=10): messages = [{"role": "user", "content": task}] for step in range(max_steps): response = client.messages.create( model="claude-sonnet-4-20250514", tools=tools, messages=messages ) if response.stop_reason == "tool_use": tool_results = execute_tools(response) messages.extend([ {"role": "assistant", "content": response.content}, {"role": "user", "content": tool_results} ]) else: return response.content[0].text When to use: General-purpose tasks — research, data analysis, customer support. 2. Router Pattern Instead of one agent doing everything, use a lightweight router to dispatch to specialized agents. class RouterAgent: def __init_