Agentic Design

Patterns
🎯

ReAct

Combines reasoning with acting through external tool use

Complexity: highReasoning Techniques

🎯 30-Second Overview

Pattern: Interleaved Reasoning and Acting through iterative Thought → Action → Observation cycles

Why: Enables dynamic problem-solving with real-time information and tool integration

Key Insight: Think before acting → Execute tools → Observe results → Reason about next step

⚡ Quick Implementation

1Think:Analyze problem & plan approach
2Act:Execute tool/function call
3Observe:Process action results
4Reason:Evaluate & plan next step
5Loop:Repeat until goal achieved
Example: Thought → search("weather NYC") → Observation: "72°F sunny" → Thought → Done

📋 Do's & Don'ts

✅Clearly separate Thought and Action phases
✅Use structured action formats (function calls)
✅Process all observations before next reasoning
✅Implement proper tool error handling
✅Set maximum iteration limits to prevent loops
❌Skip observation phase after actions
❌Use ambiguous or unstructured action formats
❌Allow infinite reasoning loops without convergence
❌Ignore tool failures or partial results
❌Mix reasoning and action in single step

🚦 When to Use

Use When

  • • Tasks requiring external data/tools
  • • Multi-step research & investigation
  • • Dynamic problem-solving workflows
  • • Real-time information needs
  • • When environment interaction is key

Avoid When

  • • Simple knowledge-based questions
  • • Tasks solvable with pure reasoning
  • • High-latency sensitive applications
  • • When tools are unreliable/unavailable
  • • Static content generation

📊 Key Metrics

Task Success Rate
Goals achieved vs attempted
Tool Utilization
Effective vs unnecessary tool calls
Reasoning Efficiency
Steps to solution convergence
Error Recovery
Success after tool failures
Information Quality
Relevance of gathered data
Iteration Count
Thought-Action cycles per task

💡 Top Use Cases

Web Research: Thought → search("AI trends 2024") → Observe results → search("specific trend") → Synthesize
Data Analysis: Thought → query_database() → Observe data → calculate_stats() → Interpret results
Code Debugging: Thought → run_tests() → Observe failures → examine_code() → Fix & retest
Travel Planning: Thought → check_flights() → Observe options → check_hotels() → Book best combo
Customer Support: Thought → lookup_account() → Observe history → check_policy() → Provide solution

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022)

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)

Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al., 2023)

WebGPT: Browser-assisted question-answering with human feedback (Nakano et al., 2021)

Implementation Guides

LangChain ReAct Agent Documentation

OpenAI Function Calling Guide

Anthropic Tool Use Documentation

Prompt Engineering Guide: ReAct Prompting

Tools & Libraries

LangChain Agent Framework

AutoGPT: Autonomous AI Agent Platform

LlamaIndex ReAct Query Engine

OpenAI Assistants API with Tools

Community & Discussions

LangChain Discord - Agent Development

OpenAI Developer Forum - Function Calling

AutoGPT Community Forum

r/MachineLearning - ReAct Discussions

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