How to choose an AI agent architecture: a decision tree from Machine Learning Mastery
Choosing an architecture for an AI agent is critical and depends on many factors. Machine Learning Mastery offers a decision tree for selecting a design pattern
When developing an AI agent, the question arises: how exactly should you structure it? One architecture suits simple classification tasks, another works better for multi-step planning. Machine Learning Mastery has proposed a decision tree that helps determine the right pattern in just a few questions.
Five
Core Patterns At the foundation of the choice lie five basic architectures: Simple Agent — an agent responsible for a single model call without loops. Suitable for quick tasks like text classification. Agent with Memory — adds dialogue history, allowing you to track context in long conversations. Tool-using Agent — can invoke functions and APIs (ReAct pattern). Needed for tasks requiring actions in external systems. Multi-agent System — multiple agents work in parallel or exchange information. Scales to complex workflows. * Hierarchical Agent — a main agent coordinates subordinates. Helps when breaking down a task into subtasks.
How to Use the Decision Tree The selection process begins with three questions.
First: do you need feedback loops and iterations, or is a single model call sufficient? If cycles are needed — go toward Tool-using or Multi-agent. Second: can one agent handle it, or does coordination require multiple? Third: what is the task complexity and scalability requirements? The answers to these questions provide a clear path through the tree to one or two patterns. Machine Learning Mastery shows how each choice affects performance, cost, and debugging simplicity.
Practical Examples For a chatbot with FAQ, a Simple Agent or Agent with Memory will suffice.
For a system that books flights and fills forms, you need a Tool-using Agent with access to airline APIs. For a corporate platform where different departments work with shared information, go with a Multi-agent system with state synchronization. The guide helps avoid overcomplication: you don't need a multi-agent system for simple classification, but a Simple Agent won't work if dynamic planning is required.
What This Means Structured architecture selection saves development time and computational costs.
Instead of trial and error — a single decision tree from experienced engineers, tested on real projects.