Multi-agent system
A multi-agent system is a network of multiple collaborating AI agents, each fulfilling a specialized role to collectively achieve complex goals.
Detailed explanation
In a multi-agent system, multiple AI agents work in parallel, each with their own specialization. Think of a sales ecosystem where one agent handles prospecting, a second writes emails, a third schedules follow-ups, and a fourth updates the CRM. Together they form a complete, autonomous sales machine. The advantage of multi-agent systems over one large agent is specialization and parallelization. Each agent can be optimized for its specific task. Moreover, they can work in parallel: while agent A searches 100 prospects, agent B is already writing emails for the first 20, and agent C is sending follow-ups for yesterday's batch. AI-agent is actually an orchestra of specialized agents: a research agent gathering company information, a writing agent formulating the perfect message, a timing agent determining the ideal send time, and an analytics agent measuring results and optimizing the approach.
Synonyms
Examples
An insurance company deploys a multi-agent system: Agent 1 processes incoming claims, Agent 2 verifies documents via API connections, Agent 3 calculates the payout, and Agent 4 communicates the result to the customer, in minutes instead of days.
When to use this?
Multi-agent systems are ideal for complex, parallelizable processes requiring different expertise, such as complete sales workflows, supply chain management, or financial analyses.
Your Company OS approach
In Your Company OS, several AI colleagues work with your team in the same environment. Each colleague has its own task and permissions; your team keeps an overview of who does what.
Related terms
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