Multi-Agent System

Technical terms
About 1 min read

An intelligent system where multiple AI agents with specialized roles collaborate to accomplish complex goals that are difficult for a single AI to solve.

Also known as
agent swarm

Detailed explanation

A multi-agent system (MAS) operates individual agents—each optimized for specific tasks like search, data analysis, or report generation—like a single team. This is similar to how different departments in a company collaborate. To prevent the performance degradation and errors that occur when a single monolithic LLM handles everything, tasks are broken down and distributed to specialized agents in each domain, with the results aggregated at the end. As of 2026, it has become a core architecture for enterprise AI adoption, demonstrating high reliability especially in tasks involving different security boundaries or complex tool-usage patterns.

Why It Matters for Tool Selection

As the number of steps increases, a single agent has a higher probability of forgetting instructions or hallucinating. Users looking to automate complex business workflows should check whether a tool goes beyond 'simple chaining' to support 'inter-agent collaboration and state persistence.' This directly impacts project success and operational costs (token usage efficiency).

Key Considerations When Selecting Workflow Tools

  • Specialization: Can independent prompts and toolsets be assigned to each agent?
  • State Persistence: If an error occurs during execution, can it recover from an intermediate step rather than restarting from the beginning?
  • Human-in-the-loop: Does it provide features for humans to step in, approve, or modify actions at critical decision-making stages?
  • Observability: Can conversations and tool calls exchanged between agents be visually tracked?

Business Application Example

When building a customer support system: 1) an 'Analysis Agent' analyzes customer sentiment and intent, 2) a 'Search Agent' finds solutions in internal databases, and 3) a 'Quality Agent' reviews the tone and accuracy of the response before generating the final answer. This dramatically reduces error rates.

Related terms

AI AgentHuman-in-the-Loop (HITL)Function CallingInference Infrastructure