What is a Multi-Agent System?

A Multi-Agent System is an architecture in which several specialised LLM agents collaborate, each with its own prompt, tool set and memory, to solve a task that exceeds the reasonable scope of a single agent. In commerce stacks this pattern lets teams compose narrow experts, such as a copywriter, a critic and a publisher, instead of relying on one bloated all-purpose prompt.

Definition

Each agent in the system is a self-contained policy: a system prompt, an allowed tool list and optional persistent memory. Coordination happens through one of several patterns: a supervisor agent that delegates to workers, a peer-to-peer message bus, a blackboard of shared state or a graph runtime with explicit edges. Agents communicate in natural language, structured JSON or both, and a controller enforces termination so the system does not loop forever. Token cost grows roughly linearly with the number of agents involved, so good designs keep each agent's context minimal and pass only the slice of state the next agent needs.

Why it matters

Splitting work across roles improves quality because each agent can be tuned and evaluated separately, and it isolates failure: a Hallucination in a draft agent is caught by a critic agent before it reaches the CMS. The pattern also maps cleanly onto microservices, which makes it a natural fit for Composable Commerce stacks already used to bounded services behind the Storefront API. The downside is emergent complexity: agents can deadlock, argue or amplify each other's mistakes, so observability and tight Guardrails are non-negotiable.

Use cases

A marketing system pairs a strategist agent that picks audience segments with a copy agent that drafts emails and a compliance agent that screens for forbidden claims. A merchandising system uses a demand-forecasting agent, a pricing agent and an inventory agent that negotiate before a price change is committed. A research system orchestrates a planner, several parallel search agents and a synthesis agent to produce market briefings, with results stored in a Knowledge Graph for later reuse.

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