AI Agent Architectures: From ReAct to Multi-Agent Systems – A Complete Guide

AI agents represent a paradigm shift from simple prompt-response interactions to autonomous systems capable of planning, reasoning, and taking actions. Understanding the architectural patterns that power these agents is essential for building production-grade AI applications.

The Agent Architecture Spectrum

Agent architectures exist on a spectrum from simple reactive systems to complex multi-agent orchestrations. Understanding where your use case fits helps you choose the right approach.

flowchart LR
    subgraph Simple["Simple Agents"]
        A[Prompt + Tools]
        B[ReAct Pattern]
    end
    
    subgraph Intermediate["Intermediate"]
        C[Planning Agents]
        D[Reflection Loops]
    end
    
    subgraph Advanced["Advanced"]
        E[Multi-Agent Systems]
        F[Hierarchical Orchestration]
    end
    
    Simple --> Intermediate --> Advanced
    
    style A fill:#E8F5E9,stroke:#A5D6A7,stroke-width:2px
    style B fill:#E8F5E9,stroke:#A5D6A7,stroke-width:2px
    style C fill:#FFF3E0,stroke:#FFCC80,stroke-width:2px
    style D fill:#FFF3E0,stroke:#FFCC80,stroke-width:2px
    style E fill:#F3E5F5,stroke:#CE93D8,stroke-width:2px
    style F fill:#F3E5F5,stroke:#CE93D8,stroke-width:2px

Figure 1: Agent architecture complexity spectrum

Pattern 1: ReAct (Reasoning + Acting)

The ReAct pattern interleaves reasoning (thinking through steps) with acting (executing tools). This simple but powerful approach forms the foundation of most agent implementations.

from openai import OpenAI
from typing import Callable
import json

class ReActAgent:
    """Simple ReAct agent implementation."""
    
    def __init__(self, client: OpenAI, tools: dict[str, Callable]):
        self.client = client
        self.tools = tools
        self.max_iterations = 10
    
    def run(self, task: str) -> str:
        messages = [
            {"role": "system", "content": self._get_system_prompt()},
            {"role": "user", "content": task}
        ]
        
        for i in range(self.max_iterations):
            response = self.client.chat.completions.create(
                model="gpt-4o",
                messages=messages,
                tools=self._get_tool_definitions(),
                tool_choice="auto"
            )
            
            message = response.choices[0].message
            
            # Check if agent wants to use a tool
            if message.tool_calls:
                messages.append(message)
                
                for tool_call in message.tool_calls:
                    result = self._execute_tool(tool_call)
                    messages.append({
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": result
                    })
            else:
                # Agent is done - return final answer
                return message.content
        
        return "Max iterations reached"
    
    def _execute_tool(self, tool_call) -> str:
        name = tool_call.function.name
        args = json.loads(tool_call.function.arguments)
        
        if name in self.tools:
            return str(self.tools[name](**args))
        return f"Unknown tool: {name}"

Pattern 2: Plan-and-Execute

For complex tasks, separating planning from execution improves reliability. The agent first creates a high-level plan, then executes each step, potentially re-planning based on results.

flowchart TB
    A[Task Input] --> B[Planner LLM]
    B --> C[Step 1]
    B --> D[Step 2]
    B --> E[Step N]
    
    C --> F[Executor]
    D --> F
    E --> F
    
    F --> G{Success?}
    G -->|Yes| H[Next Step]
    G -->|No| I[Re-plan]
    I --> B
    
    H --> J{All Done?}
    J -->|Yes| K[Final Output]
    J -->|No| F
    
    style A fill:#E3F2FD,stroke:#90CAF9,stroke-width:2px
    style B fill:#F3E5F5,stroke:#CE93D8,stroke-width:2px
    style F fill:#E8F5E9,stroke:#A5D6A7,stroke-width:2px
    style K fill:#E8F5E9,stroke:#A5D6A7,stroke-width:2px

Figure 2: Plan-and-Execute architecture with re-planning capability

from pydantic import BaseModel

class Step(BaseModel):
    description: str
    tool: str
    expected_output: str

class Plan(BaseModel):
    goal: str
    steps: list[Step]

class PlanAndExecuteAgent:
    """Agent that plans before executing."""
    
    def __init__(self, client: OpenAI, tools: dict):
        self.client = client
        self.tools = tools
        self.planner_model = "gpt-4o"
        self.executor_model = "gpt-4o-mini"
    
    async def run(self, task: str) -> str:
        # Phase 1: Create plan
        plan = await self._create_plan(task)
        
        results = []
        for i, step in enumerate(plan.steps):
            # Phase 2: Execute each step
            result = await self._execute_step(step, results)
            results.append(result)
            
            # Phase 3: Check if re-planning needed
            if self._needs_replan(step, result):
                remaining_task = self._get_remaining_task(plan, i, results)
                new_plan = await self._create_plan(remaining_task)
                plan.steps = plan.steps[:i+1] + new_plan.steps
        
        return self._synthesize_results(results)
    
    async def _create_plan(self, task: str) -> Plan:
        response = await self.client.beta.chat.completions.parse(
            model=self.planner_model,
            messages=[
                {"role": "system", "content": "Create a step-by-step plan."},
                {"role": "user", "content": task}
            ],
            response_format=Plan
        )
        return response.choices[0].message.parsed

Pattern 3: Reflection and Self-Critique

Reflection patterns allow agents to evaluate their own outputs and iterate on improvements. This is particularly valuable for creative tasks, code generation, and complex reasoning.

class ReflectiveAgent:
    """Agent with self-critique capabilities."""
    
    REFLECTION_PROMPT = """Review the following output:
{output}

Consider:
1. Does it fully address the original task?
2. Are there any errors or inconsistencies?
3. What could be improved?

Provide specific, actionable feedback."""

    async def generate_with_reflection(
        self, 
        task: str, 
        max_reflections: int = 3
    ) -> str:
        output = await self._generate(task)
        
        for i in range(max_reflections):
            critique = await self._reflect(output)
            
            if self._is_satisfactory(critique):
                break
            
            # Improve based on reflection
            output = await self._improve(output, critique)
        
        return output
    
    async def _reflect(self, output: str) -> str:
        response = await self.client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a critical reviewer."},
                {"role": "user", "content": self.REFLECTION_PROMPT.format(output=output)}
            ]
        )
        return response.choices[0].message.content

Pattern 4: Multi-Agent Systems

For complex tasks, multiple specialized agents can collaborate, each handling a specific aspect of the problem. This mirrors how human teams work.

flowchart TB
    subgraph Orchestrator["Orchestrator Agent"]
        O[Task Router]
    end
    
    subgraph Specialists["Specialist Agents"]
        A[Research Agent]
        B[Writer Agent]
        C[Critic Agent]
        D[Code Agent]
    end
    
    O --> A
    O --> B
    O --> C
    O --> D
    
    A --> O
    B --> O
    C --> O
    D --> O
    
    style O fill:#F3E5F5,stroke:#CE93D8,stroke-width:3px
    style A fill:#E3F2FD,stroke:#90CAF9,stroke-width:2px
    style B fill:#E8F5E9,stroke:#A5D6A7,stroke-width:2px
    style C fill:#FFF3E0,stroke:#FFCC80,stroke-width:2px
    style D fill:#E0F2F1,stroke:#80CBC4,stroke-width:2px

Figure 3: Multi-agent orchestration with specialized agents

Comparison: When to Use Each Pattern

Key Takeaways

• ✅ Start simple: ReAct handles 80% of agent use cases effectively
• ✅ Add planning: For tasks with 5+ steps, separate planning from execution
• ✅ Use reflection: When output quality matters more than speed
• ✅ Go multi-agent: Only when single-agent complexity becomes unmanageable
• ✅ Monitor everything: Agent behavior is harder to predict – invest in observability

Conclusion

The field of AI agent architectures is rapidly evolving, driven by advances in foundation models and a deeper understanding of how to orchestrate complex AI workflows. From the elegant simplicity of ReAct to the sophisticated coordination of multi-agent systems, each pattern offers distinct trade-offs between complexity, capability, and reliability.

For practitioners, the key insight is to start simple and add complexity only when necessary. ReAct and similar reasoning-action loops handle the majority of real-world agent use cases effectively. Plan-and-execute patterns shine for longer, more complex workflows. Reflection enables quality improvements for creative and knowledge-intensive tasks. Multi-agent systems become valuable when the problem domain naturally decomposes into specialized roles.

As you build production agent systems, invest heavily in observability and monitoring. Agent behavior is inherently less predictable than traditional software—understanding what your agents are doing, why they’re doing it, and how well they’re performing is essential for maintaining reliable systems.

References

• ReAct: Synergizing Reasoning and Acting in Language Models – Original ReAct paper from Google Research
• Reflexion: Language Agents with Verbal Reinforcement Learning – Research on self-reflective agents
• LLM Powered Autonomous Agents – Lilian Weng’s comprehensive survey
• LangGraph Documentation – Framework for building stateful agent workflows
• Microsoft AutoGen – Multi-agent conversation framework
• CrewAI Documentation – Role-playing multi-agent framework
• MetaGPT: Meta Programming for Multi-Agent Collaborative Framework – Research on software development agents