Agentic AI Patterns : Demystifying ReAct, Reflexion and Auto-GPT

ReAct (Reason + Act), Reflexion, and Auto-GPT. While all three are iterative patterns, but each focuses on a different aspect of AI agent design.

ReAct (Reason + Act)

• Primary Goal: Solve complex tasks by dynamically using tools (web searches, calculators, code runners) in a loop of reasoning and action.

Reflexion

• Primary Goal: Improve correctness by having the model “look back” at its own output (or errors) and refine it.

Auto-GPT

• Primary Goal: Multi-step autonomy — the agent can set new sub-tasks, gather data from multiple sources, and pivot strategies as needed.

ReAct: Great for stepwise problem solving where the model must call tools repeatedly within a single conversation.

Reflexion: Best for self-improving or self-correcting a single solution (e.g., code snippet or text).

Auto-GPT: Best for longer, open-ended tasks requiring repeated planning, tool usage, and memory management.