A multi-agent system is a group of AI agents that each handle a specific task and pass results to one another. Instead of prompting one model to do everything, you split the work — a researcher finds facts, a writer drafts content, an editor reviews it.

Originally published at kalyna.pro

Why Multi-Agent?

Single-agent LLM calls hit limits quickly:

Context window overflow — one agent can't hold a 200-page report and write a summary simultaneously

Quality degradation — researching AND writing AND fact-checking in one prompt produces mediocre results

No parallelism — agents can run simultaneously; sequential prompts cannot

Hard to debug — when one big prompt fails, you don't know which step broke

Core Architecture Patterns

1. Orchestrator → Workers

Orchestrator ├── Research Agent → gathers data ├── Analysis Agent → interprets data └── Writer Agent → produces output

Enter fullscreen mode

Exit fullscreen mode

2.…