Concept: ReAct Pattern for AI Agents
What is ReAct?
ReAct (Reasoning + Acting) is a framework that combines:
- Reasoning: Thinking through problems step-by-step
- Acting: Using tools to accomplish subtasks
- Observing: Learning from tool results
This creates agents that can solve complex, multi-step problems reliably.
The Core Pattern
┌─────────────┐
│ Problem │
└──────┬──────┘
│
▼
┌─────────────────────────────────────┐
│ ReAct Loop │
│ │
│ ┌──────────────────────────────┐ │
│ │ 1. THOUGHT │ │
│ │ "What do I need to do?" │ │
│ └─────────────┬────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────┐ │
│ │ 2. ACTION │ │
│ │ Call tool with parameters │ │
│ └─────────────┬────────────────┘ │
│ ▼ │
│ ┌──────────────────────────────┐ │
│ │ 3. OBSERVATION │ │
│ │ Receive tool result │ │
│ └─────────────┬────────────────┘ │
│ │ │
│ └──► Repeat or │
│ Final Answer │
└─────────────────────────────────────┘
Why ReAct Matters
Traditional LLMs Struggle With:
- Complex calculations - arithmetic errors
- Multi-step problems - lose track of progress
- Using tools - don't know when/how
- Explaining decisions - black box reasoning
ReAct Solves This:
- Reliable calculations - delegates to tools
- Structured progress - explicit steps
- Tool orchestration - knows when to use what
- Transparent reasoning - visible thought process
The Three Components
1. Thought (Reasoning)
The agent reasons about:
- What information is needed
- Which tool to use
- Whether the result makes sense
- What to do next
Example:
Thought: I need to calculate 15 × 8 to find revenue
2. Action (Tool Use)
The agent calls a tool with specific parameters:
Example:
Action: multiply(15, 8)
3. Observation (Learning)
The agent receives and interprets the tool result:
Example:
Observation: 120
Complete Example
Problem: "If 15 items cost $8 each and 20 items cost $8 each,
what's the total revenue?"
Thought: First I need to calculate revenue from 15 items
Action: multiply(15, 8)
Observation: 120
Thought: Now I need revenue from 20 items
Action: multiply(20, 8)
Observation: 160
Thought: Now I add both revenues
Action: add(120, 160)
Observation: 280
Thought: I have the final answer
Answer: The total revenue is $280
Key Benefits
1. Reliability
- Tools provide accurate results
- No arithmetic mistakes
- Verifiable calculations
2. Transparency
- See each reasoning step
- Understand decision-making
- Debug easily
3. Scalability
- Handle complex problems
- Break into manageable steps
- Add more tools as needed
4. Flexibility
- Works with any tools
- Adapts to problem complexity
- Self-corrects when needed
Comparison with Other Approaches
Zero-Shot Prompting
User: "Calculate 15×8 + 20×8"
LLM: "The answer is 279" ❌ Wrong!
Problem: LLM calculates in head, makes errors
Chain-of-Thought
User: "Calculate 15×8 + 20×8"
LLM: "Let me think step by step:
15×8 = 120
20×8 = 160
120+160 = 279" ❌ Still wrong!
Problem: Shows work but still miscalculates
ReAct (This Implementation)
User: "Calculate 15×8 + 20×8"
Agent:
Thought: Calculate 15×8
Action: multiply(15, 8)
Observation: 120
Thought: Calculate 20×8
Action: multiply(20, 8)
Observation: 160
Thought: Add results
Action: add(120, 160)
Observation: 280
Answer: 280 ✅ Correct!
Success: Uses tools, gets accurate results
Architecture Diagram
┌──────────────────────────────────────┐
│ User Question │
└──────────────┬───────────────────────┘
│
▼
┌──────────────────────────────────────┐
│ LLM with ReAct Prompt │
│ │
│ "Think, Act, Observe pattern" │
└──────┬───────────────────────────────┘
│
├──► Generates: "Thought: ..."
│
├──► Generates: "Action: tool(params)"
│ │
│ ▼
│ ┌─────────────────┐
│ │ Tool Executor │
│ │ │
│ │ - multiply() │
│ │ - add() │
│ │ - divide() │
│ │ - subtract() │
│ └─────────┬───────┘
│ │
│ ▼
└───────── "Observation: result"
│
├──► Next iteration or Final Answer
│
▼
┌──────────────────────────────────────┐
│ Final Answer │
└──────────────────────────────────────┘
Implementation Strategies
1. Explicit Pattern Enforcement
Force the LLM to follow structure:
systemPrompt: `CRITICAL: Follow this EXACT pattern:
Thought: [reasoning]
Action: [tool call]
Observation: [result]
...
Answer: [final answer]`
2. Iteration Control
Prevent infinite loops:
maxIterations = 10 // Safety limit
3. Streaming Output
Show progress in real-time:
onTextChunk: (chunk) => {
process.stdout.write(chunk);
}
4. Answer Detection
Know when to stop:
if (response.includes("Answer:")) {
return fullResponse; // Done!
}
Real-World Applications
1. Math & Science
- Complex calculations
- Multi-step derivations
- Unit conversions
2. Data Analysis
- Query databases
- Process results
- Generate reports
3. Research Assistants
- Search multiple sources
- Synthesize information
- Cite sources
4. Coding Agents
- Read code
- Run tests
- Fix bugs
- Refactor
5. Customer Support
- Query knowledge base
- Check order status
- Process refunds
- Escalate issues
Limitations & Considerations
1. Iteration Cost
Each thought/action/observation cycle costs tokens and time.
Solution: Use efficient models, limit iterations
2. Tool Quality
ReAct is only as good as its tools.
Solution: Build robust, well-tested tools
3. Prompt Engineering
System prompt must be very clear.
Solution: Test extensively, iterate on prompt
4. Error Handling
Tools can fail or return unexpected results.
Solution: Add error handling, validation
Advanced Patterns
Self-Correction
Thought: That result seems wrong
Action: verify(previous_result)
Observation: Error detected
Thought: Let me recalculate
Action: multiply(15, 8) # Try again
Meta-Reasoning
Thought: I've used 5 iterations, I should finish soon
Action: summarize_progress()
Observation: Still need to add final numbers
Thought: One more step should do it
Dynamic Tool Selection
Thought: This is a division problem
Action: divide(10, 2) # Chooses right tool
Thought: Now I need to add
Action: add(5, 3) # Switches tools
Research Origins
ReAct was introduced in:
"ReAct: Synergizing Reasoning and Acting in Language Models"
Yao et al., 2022
Paper: https://arxiv.org/abs/2210.03629
Key insight: Combining reasoning traces with task-specific actions creates more powerful agents than either alone.
Modern Frameworks Using ReAct
- LangChain - AgentExecutor with ReAct
- AutoGPT - Autonomous task execution
- BabyAGI - Task management system
- GPT Engineer - Code generation
- ChatGPT Plugins - Tool-using chatbots
Why Learn This Pattern?
1. Foundation of Modern Agents
Nearly all production agent systems use ReAct or similar patterns.
2. Understandable AI
Unlike black-box models, you see exactly what's happening.
3. Extendable
Easy to add new tools and capabilities.
4. Debuggable
When things go wrong, you can see where and why.
5. Production-Ready
This pattern scales from demos to real applications.
Summary
ReAct transforms LLMs from:
- Brittle calculators → Reliable problem solvers
- Black boxes → Transparent reasoners
- Single-shot answerers → Iterative thinkers
- Isolated models → Tool-using agents
It's the bridge between language models and autonomous agents that can actually accomplish complex tasks reliably.