AI Agent Design Patterns — Architecture Guide 2026

What it is

This resource is a curated catalog of design patterns for building AI agents. It covers the architectural approaches that have emerged as standard practice: ReAct (reason + act), Plan-and-Execute, Reflection loops, Tool Use, Multi-Agent orchestration, and Human-in-the-Loop control flows. Each pattern includes when to use it, how it works, and implementation guidance.

The guide targets AI engineers, developers building agent systems, and architects deciding which patterns fit their use case. It provides a decision framework rather than promoting a single approach.

How it saves time or tokens

Building an AI agent without understanding established patterns leads to reinventing solutions that the community has already refined. This catalog maps task types to appropriate patterns, so you pick the right architecture from the start instead of discovering its limitations mid-project. Choosing the correct pattern also minimizes wasted LLM tokens -- a ReAct agent uses fewer tokens than Plan-and-Execute for simple tasks, while multi-step projects need the planning overhead.

How to use

1. Identify your task complexity using the decision table:

1. Study the pattern description, architecture diagram, and code example.

1. Implement using your preferred framework (LangGraph, OpenAI Agents SDK, CrewAI, etc.).

Example

# ReAct pattern: Reason -> Act -> Observe -> Repeat
def react_agent(query, tools, max_steps=5):
    messages = [{'role': 'user', 'content': query}]
    for step in range(max_steps):
        response = llm.chat(messages, tools=tools)
        if response.tool_calls:
            for call in response.tool_calls:
                result = execute_tool(call)
                messages.append({'role': 'tool', 'content': result})
        else:
            return response.content
    return 'Max steps reached'

Related on TokRepo

• Multi-Agent Frameworks -- Compare frameworks for building multi-agent systems
• AI Tools for Agents -- Tools and libraries for building AI agents

Common pitfalls

• Over-engineering with multi-agent patterns when a single ReAct agent suffices. Start simple and add complexity only when the task demands it.
• Skipping the human-in-the-loop pattern for high-stakes decisions. Autonomous agents making irreversible actions (deploys, payments, emails) need approval gates.
• Ignoring token costs in reflection loops. Each reflection cycle consumes additional tokens. Set a maximum iteration count to prevent runaway costs.