Multi-Agent Framework Design: Planning, Control, and Agent Behavior

A visual guide to help teams design better multi-agent LLM systems—based on task planning, control structure, and agent ability.

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:

  1. Task Planning – Will a central system decompose goals, or will agents self-organize?
  2. Control Flow – Will you use a hierarchical, equi-level, or adaptive strategy to govern decisions?
  3. 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.

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