Single-Agent Versus Multi-Agent Architectures.

Single-Agent architectures have a single AI agent that independently resolves tasks.

When building an agentic system you can build either a single-agent or a multi-agent

architecture. Agentic AI systems use an LLM as the brain of the operation. This LLM has access

to tools. At any given time, the LLM evaluates whether a tool is useful to solve (a part of) the

query. This is referred to as a 'single-agent architecture'.

However, it also sometimes makes sense to initialize multiple agents, each responsible of solving a certain group of tasks. These are called 'multi-agent architectures'. Often, we may still have one agent (LLM) acting as the lead of the whole operation: the main agent.

The choice depends on your use case and how complex the required agent actions are.

Strengths:

• Low complexity and thus easier to develop and manage.
• No coordination between multiple agents required.
• May require fewer computational resources for a single powerful agent than

multiple less powerful agents.

Weakness:

• May struggle with complex or dynamic tasks.
• Limited in handling tasks that require collaboration or diverse expertise.
• Agent can get confused and use incorrect tool call arguments if the agent has

too many different tool options available.

• May require a larger, more expensive model to handle multiple reasoning steps.

Note that each agent is equipped with its own memory here. However, you can also have memory for the compositional agentic architecture.

Strengths:

• Capable of handling complex and dynamic tasks.
• Capable of parallel processing for efficiency.
• Possible to use smaller models specialized for distinct tasks.

Weakness:

• Increased complexity due to multiple agents collaborating with each other.
• Requires robust mechanisms to manage interactions.
• Harder to debug and optimize due to added complexity.
• May require more resources as more agents are added to the system.

As you can see both, single-agent and multi-agent architectures have both strengths and weaknesses. Single-agent architectures are ideal when the task is straightforward and well-defined and you don’t have specific resource constraints. On the other hand, multi-agent architectures are helpful when the use case is complex and dynamic, requires more specialized knowledge and collaboration, or has scalability and adaptability requirements. For example, Manus AI is built on a multi-agent architecture.