Multi-Agent System

What Is a Multi-Agent System?

A multi-agent system (MAS) is an AI architecture composed of multiple independent agents, each specializing in a particular capability or domain, that collaborate to solve a problem too complex for any single agent to handle alone. Rather than building one monolithic agent that does everything, you build a team of focused agents that divide the work.

This mirrors how human organizations operate: a company does not have one person who does everything — it has designers, engineers, marketers, and managers who each contribute their expertise toward a shared goal.

Why Use Multiple Agents?

A single agent running in a long loop faces several practical limits:

• Context window constraints — A very long task fills the context window, degrading performance.
• Error compounding — Mistakes early in a long chain pollute later reasoning.
• Lack of specialization — A generalist agent makes tradeoffs that a specialist would not.

Multi-agent systems address all three by decomposing large tasks into bounded sub-tasks, each handled by an agent optimized for it.

Common Architectural Patterns

Hierarchical (Orchestrator + Workers)

An orchestrator agent receives the high-level goal, decomposes it into sub-tasks, dispatches each sub-task to a specialized worker agent, and assembles the final result from their outputs.

Peer-to-Peer

Agents communicate directly with each other, passing results and requests without a central coordinator. This is more flexible but harder to debug.

Pipeline

Each agent's output becomes the next agent's input in a fixed sequence. Simple and predictable, but less adaptive.

A Multi-Agent Example: Competitive Intelligence

Consider a system that analyzes five competitors and writes a market report:

• Researcher Agent — Uses KnowledgeSDK's /v1/sitemap to discover all pages on each competitor's site, then /v1/scrape to pull their content.
• Extractor Agent — Calls /v1/extract on each page to produce structured data: products, pricing, features.
• Analyst Agent — Compares the structured data across competitors and identifies differentiators.
• Writer Agent — Synthesizes the analysis into a formatted report.
• Orchestrator Agent — Coordinates the above agents, passes results between them, and delivers the final report to the user.

Each agent does one thing well. The orchestrator handles coordination, not domain expertise.

Communication Between Agents

Agents communicate through:

• Shared memory or storage — Results are written to a common store that other agents can read.
• Direct message passing — One agent sends its output directly as input to the next.
• Tool calls — One agent exposes itself as a callable tool that another agent can invoke.

Challenges

• Coordination overhead — More agents means more orchestration logic to write and debug.
• Error propagation — A bad output from one agent can cascade through the system.
• Cost — Multiple LLM calls per task increases token expenditure.

Despite these challenges, multi-agent systems are currently the most effective approach for tackling long-horizon, complex tasks with AI.