Single-Agent vs Multi-Agent AI Comparison
Single-Agent vs Multi-Agent AI Comparison
https://integrail.ai/blog/single-agent-vs-multi-agent-ai-comparison
Choosing the Right System
The choice between single-agent and multi-agent systems depends on the specific requirements of your project:
- For Simple, Well-Defined Tasks: A single-agent system is usually sufficient and more cost-effective.
- For Complex, Dynamic Environments: A multi-agent system offers the flexibility and robustness needed to manage intricate and evolving challenges.
What is a multiagent system?
https://www.ibm.com/think/topics/multiagent-system
Single agent intelligent systems engage with their environment to autonomously plan, call tools and produce responses. The tools made available to an agent provide information that is otherwise unavailable to the agent. As previously described, this information can be a database acquired through an API or another agent. There is a distinction here between single and multiagent systems. When calling another agent as a tool, that secondary agent is part of the original agent’s environmental stimuli. That information is acquired and no further cooperation takes place. Whereas multiagent systems differ by involving all agents within the environment to model each other’s goals, memory and plan of action.4 Communication between agents can be direct or indirect through altering the shared environment.
Each entity within a multiagent system is an autonomous agent to some extent. This autonomy is typically seen by the agent’s planning, tool calling and general reasoning. In a multiagent system, agents remain autonomous but also cooperate and coordinate in agent structures.3 To solve complex problems, agent communication and distributed problem-solving are key. This type of agent interaction can be described as multiagent reinforcement learning. The information shared through this form of learning can include instantaneous information acquired through sensors or actions. Additionally, an agent’s experiences in the form of episodic information can be shared. These episodes can be sequences of sensations, actions and learned policies. Finally, agents can share their experiences in real-time to prevent other agents from repetitively learning the same policies.5
Individual agents are powerful on their own. They can create subtasks, use tools and learn through their interactions. The collective behavior of multiagent systems increases the potential for accuracy, adaptability and scalability. Multiagent systems tend to outperform single-agent systems due to the larger pool of shared resources, optimization and automation. Instead of multiple agents learning the same policies, one can share learned experiences to optimize time complexity and efficiency.5
思维融入工作流
https://mp.weixin.qq.com/s/6FANreQTlkD3SXHCxfSD3g
