What is the difference between normal AI agents vs multi-AI agents?

Normal AI Agents vs Multi-AI Agents

As tasks become more complex, a single agent may not be sufficient. Multi-agent systems distribute work across specialized agents that collaborate.

Definitions

Single Agent Architecture

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# One agent handles the full task
class SingleAgent:
    def run(self, task: str):
        plan = self.llm.plan(task)
        for step in plan.steps:
            result = self.execute(step)
        return result

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User → [Single Agent] → Final Result
              ↕
           [Tools]

Multi-Agent Architecture

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# Multiple agents collaborate
from crewai import Agent, Task, Crew

researcher = Agent(role="Researcher", tools=[web_search])
analyst = Agent(role="Data Analyst", tools=[python_repl])
writer = Agent(role="Writer", tools=[file_write])
reviewer = Agent(role="Reviewer")

crew = Crew(
    agents=[researcher, analyst, writer, reviewer],
    process="hierarchical",  # or sequential
    manager_llm=gpt4
)

crew.kickoff(inputs={"topic": "AI trends analysis"})

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                  [Orchestrator]
                /       |            [Researcher]  [Analyst]   [Writer]
          ↓           ↓           ↓
      Web data    Data viz    Draft report
                  \       |       /
                 [Reviewer Agent]
                        ↓
                  Final Report

When to Use Multi-Agent Systems

Patterns in Multi-Agent Systems

Challenges with Multi-Agent Systems

Recommendation

Start with a single agent and only move to multi-agent when:

1. Task complexity exceeds single context window
2. Different domains require specialized tools
3. Parallel execution would significantly reduce latency
4. Quality control requires a separate review step