AI is everywhere these days, and it makes our lives easier and our businesses more efficient. But when it comes to making the most of AI tools, there is still much grunt work involved. Multi-agent AI teams (or agentic systems) are the paradigm shift in human-AI interactions, and they’re growing fast.
In today’s article, we compare single-agent systems versus multi-agent AI teams. You’ll learn how each of them works and where they fit into your business and personal life. We’ll also show you how you can use Taskade to build, train, and deploy your own AI workforce of the future. 🤖🤖
Psst… New to the topic? Check our article What Are AI Agents? first.
👤 What Is a Single-Agent AI System?
A single-agent AI system is a type of agentic system built on top of one, specialized autonomous AI agent. It can work as a standalone tool or as part of an integrated platform and usually packages additional components like agent knowledge and integrations with external tools.
The main purpose of such a system is to perform tasks independently in self-directed loops. This is a step up from “traditional” prompt-based AI interactions where you continuously feed AI instructions.
Aside from partial autonomy, agentic systems are specialized and can provide more tailored responses than generalized chats with large language models (LLMs) like GPT-4o, Gemini, or Bard. This makes them particularly useful for specific, complex tasks that require a deep understanding of niche topics.
👥 What Is a Multi-Agent AI System?
A multi-agent AI system is made up of several AI agents working together to achieve set goals. Each agent focuses on a specific area within the larger business context, just like in human teams.
Similar to single-agent AI systems, multi-agent setups can tap into a range of external tools and use them as needed to carry out specific tasks. You can also fine-tune each “member” of an AI team with new knowledge using documents, web pages, and other resources for more tailored responses.
So, what makes multi-agent AI systems different?
Unlike simple agentic systems, AI teams can tackle tasks that require adaptive learning, sequential thinking, and enhanced fault tolerance. Since each agent plays a unique role, they can interact naturally, distribute the workload, and hand off tasks in dynamic and complex scenarios.
⚡ Key Differences Between Single-Agent and Multi-Agent Systems
Single agent systems versus multi-agent AI teams — how do these two differ? which is better? Check the table below for a quick overview or scroll down for a detailed comparison.
Check out the official Taskade blog to find out the difference between single and multi-agent systems.
