AI Agent Architecture Patterns: A Developer's Guide

What Are AI Agents?

AI agents are systems that can perceive their environment, make decisions, and take actions to achieve goals. They go beyond simple prompt-response interactions.

Pattern 1: ReAct (Reason + Act)

The model alternates between reasoning about what to do and executing actions. Best for tool-using applications.

• Think → Act → Observe → Think → Act → ...
• Simple, effective, widely used.

Pattern 2: Plan-and-Execute

First create a complete plan, then execute each step. Better for complex multi-step tasks.

• Plan: Break task into ordered subtasks.
• Execute: Complete each subtask sequentially.
• Re-plan: Adjust the plan based on intermediate results.

Pattern 3: Multi-Agent Collaboration

Multiple specialized agents work together, each handling a different aspect of the problem.

• Orchestrator: Coordinates other agents.
• Specialists: Coding agent, research agent, review agent.
• Communication via shared state or message passing.

Pattern 4: Reflection / Self-Critique

The agent reviews its own output and iterates to improve quality.

• Generate → Critique → Revise → Critique → Final output.

Pattern 5: Human-in-the-Loop

Agent proposes actions but requires human approval for critical decisions.

Choosing a Pattern

• Simple tool use: ReAct
• Complex workflows: Plan-and-Execute
• Large projects: Multi-Agent
• High-stakes output: Reflection + Human-in-the-Loop