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Agents created on the VIVI platform can be integrated with other agents, enabling multi-agent systems. For most use cases, a single agent is more efficient than a multi-agent system. However, a multi-agent system can be ideal for complex workflows that require specialized sub-agents. The choice between these approaches depends on your task complexity and scaling needs. We recommend starting with a single-agent workflow to scope out your use case, and scaling to a multi-agent approach only when your logic becomes too complex for a single agent to manage effectively.

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Single-Agent Approach

In a single-agent system, one agent handles the full workflow of interpreting a user’s input, calling tools, reasoning, and generating output. It operates under a single identity and prompt. Pros:
  • Simpler to implement, deploy, and maintain.
  • Fewer coordination concerns as there is no need for agent-to-agent communication.
  • Faster decision-making loop since context stays within one agent.
  • Easier debugging and monitoring.
Cons:
  • Multi-tool or multi-domain reasoning can degrade performance as the agent struggles to manage context.
  • Scalability is limited as all logic lives in a single agent.
  • Harder to create specialization or modular upgrades.
A single-agent approach works best for linear, well-scoped workflows such as automating data classification or simple integrations where reasoning and tool use are predictable. It minimizes automation costs while maintaining high reliability.

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Multi-Agent Approach

In a multi-agent system, there are multiple specialized agents, each designed to handle specific tasks or roles. Agents are called as tools by the supervisor agent, which is the agent that will handle user interactions and deliver the final output. Pros:
  • Specialization allows for each agent to focus on a narrow task, which can improve overall quality.
  • Multiple agents allow for the system to be modular, so you can plug-in, remove, or upgrade agents without re-writing the entire system.
  • If one agent fails, the remainder of the system will continue to operate successfully.
Cons:
  • Requires managing communication, context hand-offs, and monitoring across agents.
  • More agents may lead to increased costs as the amount of tool calls and AI messages increase.
  • Debugging becomes more difficult since failures may occur in agent communication rather than logic.
A multi-agent setup is best for complex, evolving, or multi-domain workflows such as knowledge retrieval, reasoning, and task execution across multiple integrations. Use this model when you expect system growth, need reusability across workflows, or require fine-grained control over how tasks are distributed.

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Best Practices

  • Ensure that you use clear names and descriptions on each of your agents to assist the supervisor agent with using its sub-agents effectively. This will ensure it uses the right tools every time.
  • Review your use-case to verify if a single or multi agent workflow would be ideal.