When should I use multi-agent vs single agent?

Use multi-agent for complex tasks benefiting from specialisation, workflows requiring different capabilities, or when you want to use different models for different steps. Single agents are simpler and sufficient for straightforward tasks.

How do agents communicate?

Agents typically communicate through shared state, message passing, or direct function calls. The orchestration layer manages communication flow and determines which agent handles each part of a task.

Is multi-agent more expensive?

Usually yes - you're running multiple model calls. However, using smaller specialised models for each task can sometimes be cheaper and better than one large model trying to do everything.

What are common multi-agent frameworks?

Popular frameworks include AutoGen (Microsoft), CrewAI, LangGraph, and custom orchestration. Each offers different approaches to defining agents and managing their interactions.

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Multi-Agent Systems

Architectures where multiple AI agents collaborate, each with specialised roles, to accomplish complex tasks through coordination.

In-Depth Explanation

Multi-agent systems use multiple AI agents working together, each with specialised capabilities, to tackle complex tasks that would be difficult for a single agent. Like a team of specialists, different agents handle different aspects of a problem.

Common multi-agent patterns:

Division of labour: Different agents handle different subtasks
Debate/consensus: Agents propose solutions and reach agreement
Hierarchical: Manager agents coordinate worker agents
Pipeline: Agents process sequentially, passing results along
Competitive: Agents compete, with best results selected

Example multi-agent workflows:

Research: One agent searches, another evaluates, another synthesises
Content creation: Writer, editor, fact-checker agents
Coding: Architect, coder, reviewer, tester agents
Customer service: Triage, specialist, escalation agents

Benefits of multi-agent approaches:

Specialisation improves quality in each domain
Complex tasks broken into manageable pieces
Different agents can use different models
Easier to debug and improve individual components
Natural parallelisation for speed

Business Context

Multi-agent systems can handle complex workflows like "research, analyse, summarise, present" by delegating to specialised agents.

How Clever Ops Uses This

We design multi-agent systems for Australian businesses handling complex workflows - from automated research to multi-step document processing to intelligent customer service escalation.

Example Use Case

"A research agent finds information, an analyst agent evaluates it for relevance and accuracy, and a writer agent creates the final report."

Frequently Asked Questions

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