Overview
Multi-agent LLM systems are powerful—but designing them well takes more than just chaining prompts.
This visual guide walks through the three core decisions that define how your agents collaborate:
- Task Planning – Will a central system decompose goals, or will agents self-organize?
- Control Flow – Will you use a hierarchical, equi-level, or adaptive strategy to govern decisions?
- Agent Ability – Do your agents simply follow instructions, or do they deliberate, collaborate, and act?
We also include considerations around:
- Information Flow (asynchronous messaging, pub/sub, queues)
- Memory Design (short-term, long-term, episodic, external)
- Real-world frameworks like LangChain, AutoGen, LangGraph, and CrewAI
Why it matters
As multi-agent systems become more common in LLM workflows, poorly scoped architectures are becoming a bottleneck. This guide is meant to help teams move beyond basic orchestration toward systems that are collaborative, adaptive, and scalable.
