Multi-Agent System
An architecture in which multiple AI agents, each with a defined role or specialization, collaborate, communicate, or operate in parallel to accomplish tasks that are too complex or broad for a single agent.
What It Is
A multi-agent system distributes cognitive work across specialized units. Rather than asking one model to understand your product catalog, your pricing rules, your customer history, and your logistics constraints simultaneously, you build separate agents for each domain and coordinate them through an orchestration layer.
Agents in a multi-agent system can communicate with each other, share outputs, hand off subtasks, and run in parallel. A catalog agent returns product options. A pricing agent applies account-specific rates to those options. A fulfillment agent checks availability at the right warehouse. A drafting agent assembles the results into a quote. Each does one thing well. Together they handle the whole workflow.
The architecture mirrors how high-performing human teams work: specialists coordinate through a shared goal, a defined handoff protocol, and clear accountability for their piece of the result.
In B2B Commerce Context
B2B commerce operations span too many domains for a single general-purpose model to cover reliably. Product data alone, including specifications, compatibility matrices, regulatory classifications, and substitute relationships, can be a domain in itself.
Pricing logic, with its account tiers, volume breaks, contract exceptions, and promotional overlays, is another. A multi-agent system assigns each domain to a specialist.
An early-stage example
As deployments mature, the architecture expands to include a pricing agent, an inventory agent, an account history agent and a compliance agent. Each runs independently, is updated independently when that domain changes, and fails independently without taking down the whole system.
When You Need It
- A single agent consistently fails on tasks that require knowledge from more than one domain.
- You have AI use cases that could run in parallel, checking inventory and calculating pricing simultaneously rather than sequentially, and latency matters.
- Different parts of your workflow change at different rates. A pricing agent needs updating when pricing rules change. That should not require redeploying your catalog agent.
- You need fault isolation: if the inventory agent fails, the quote process should continue with an availability caveat rather than failing completely.
Start with two agents before building five. The coordination overhead is real. Add agents when you have a clear domain boundary that justifies it.
What It Is Not
- A multi-agent system is not simply multiple AI features running in an application. Features that do not communicate, share state, or coordinate are not a system. A multi-agent system has defined communication protocols between agents and a shared understanding of the goal they are working toward.
- It is not inherently more reliable than a single agent. More components mean more failure points. A multi-agent system is more reliable per domain, since each specialist is better at its task, but more complex to debug when something goes wrong in the coordination layer.
- It is not the right architecture for every problem. If your AI use case is genuinely single-domain, such as a product search assistant that only needs product data, building a multi-agent system adds complexity without benefit. The architecture should follow the problem, not the other way around.
Comparison
| Agent | Domain | Typical task in B2B commerce |
| Front-end / intent agent | Customer interaction | Classifies query, routes to specialists |
| Catalog agent | Product knowledge | Finds SKUs, checks compatibility, suggests substitutes |
| Pricing agent | Account pricing rules | Applies contract rates, volume breaks, promotions |
| Inventory agent | Stock and fulfillment | Checks availability, suggests alternative warehouse |
| Compliance agent | Regulatory and policy | Validates order against export rules or purchase limits |
See also
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