Multi-Agent System

A single agent can handle a lot, but as tasks get longer its context window fills with details from every step, and quality drops. Splitting the work lets each agent focus on one job with a clean, relevant context, much like a team where each person has a clear responsibility.

How Does a Multi-Agent System Work?

The most common pattern is a lead agent that plans the task and delegates parts of it to sub agents. Each sub agent works on a narrow piece, such as research, writing or checking, and returns a short summary rather than its full working notes. The lead agent combines the results. Our post on context engineering explains why this keeps each agent's context small and focused.

Common Multi-Agent Patterns

• Lead and sub agents: one agent plans and delegates, others execute.
• Pipeline: agents run in sequence, each taking the previous output as input.
• Reviewer: one agent produces work and another critiques or verifies it before it is accepted.
• Parallel: several agents explore different options at the same time, then results are compared.

Multi-Agent System vs Orchestration

A multi-agent system describes who does the work: several agents. AI orchestration describes how the work is coordinated: the order of steps, handoffs, error handling and when to stop for human review. Every reliable multi-agent system needs orchestration, whether it is built with a framework such as LangGraph or CrewAI or with a simpler workflow tool.

When Is a Multi-Agent System Worth It?

Multi-agent setups use more tokens and are harder to debug than a single agent, so they are worth it when a task is genuinely large or has clearly separate parts, such as researching many sources at once, or when an independent reviewer step meaningfully improves quality. For short, simple tasks, one well prompted agent is usually faster, cheaper and easier to trust.