Module Summary

The ReAct pattern combines reasoning (thinking) with acting (doing) in an iterative loop. This synergy enables AI agents to:

Make decisions transparently through explicit thought traces
Ground reasoning in real-world observations from actions
Adapt dynamically when unexpected results occur
Handle complex, multi-step tasks that require exploration

Studies show ReAct improves task success rates by 20-40% compared to reasoning-only or action-only baselines. It's become a foundational pattern in modern agentic AI systems.

Quick Reference

When to Use ReAct

✓ Multi-step reasoning tasks
✓ Tasks requiring tool/API usage
✓ Dynamic environments
✓ Need for explainability

Implementation Tips

✓ Structure thought prompts clearly
✓ Provide diverse tool options
✓ Set max iteration limits
✓ Log full reasoning traces

What's Next?

You've mastered the ReAct pattern! Continue your learning journey with these related topics:

📋 Plan-and-Execute

Learn how to create upfront plans before executing actions

🔄 Replanning

Discover how agents adapt plans when things don't go as expected

🎯 Goal-Oriented Planning

Explore backward chaining from goals to actions

🛠️ Tool Use

Deep dive into how agents select and use external tools

ReAct Pattern

Your Progress

Review Checklist