# What is multi-agent AI?

> Multi-agent AI is a system in which several specialised AI agents work together on a task — for example one agent gathers information, another checks rules and a third drafts the result — coordinated by an orchestrator or by passing work between them. It suits complex work that one agent would handle poorly.

By Vijay Sharma, Agentic AI Tech Hub · Updated October 8, 2026 · https://www.agenticaitechhub.com/blog/what-is-multi-agent-ai

## Common patterns

- **Orchestrator and specialists** — a lead agent splits the task and assigns parts to specialist agents.
- **Pipeline** — each agent does one stage and passes the result on.
- **Reviewer** — one agent produces, another checks quality or compliance.

## When is multi-agent AI worth it?

When a process has clearly different skills or steps (research, analysis, writing, checking), large volumes, or strict review needs. For a single, well-defined job, one agent with good tools is simpler, cheaper and easier to test.

## What does it cost?

A platform with several agents, enterprise integrations and data pipelines is typically a **Large** project at Agentic AI Tech Hub — $20,000–$45,000 one-time, usually 6–9 months — as a rough guide. See [AI agent development cost](https://www.agenticaitechhub.com/blog/ai-agent-development-cost).

## Frequently asked questions

### Is multi-agent AI more accurate?

It can be, when specialised agents and a reviewer catch each other’s mistakes, but it also adds complexity that must be tested.

### Do agents share memory?

They can share task state through the orchestrator or a common store, with permissions limiting what each one sees.