Multi-Agent System (MAS)

Why multi-agent systems matter

Most real business workflows are not single steps. A customer-support workflow needs to: classify the inquiry, look up the customer, find the relevant policy, draft a response, check it against tone guidelines, decide if escalation is needed, and send. Trying to do all of that inside one agent’s context produces a fragile, unfocused system that is hard to evaluate and harder to fix.

A multi-agent system breaks the workflow into specialists. Each specialist has a tight prompt, a small tool surface, and a clean context. The system gains accuracy, parallelism, and debuggability, the trade is in orchestration cost and additional design care.

How theagency47 builds multi-agent systems

Our Workforce Pro tier is built around multi-agent systems by default, five coordinated agents covering one full department. The most common patterns we ship:

• Supervisor pattern. A lead agent receives the request, decides which specialist agent to invoke, integrates results, and returns the final answer. Used in our SEO/GEO industry pack, where a lead agent orchestrates keyword research, content briefing, copy, schema, and audit specialists.
• Router pattern. An incoming request is classified and routed to the right specialist without a coordinating layer. Useful for customer-support workflows where the bulk of the work is “which queue does this belong in.”
• Pipeline pattern. Work flows through a fixed sequence of agents (research → draft → review → publish), each handing off to the next. The simplest pattern; the right call when the workflow shape is stable.
• Handoff pattern. Agents dynamically transfer work to peers when their confidence is low. Used in Scale package builds where escalation paths are unpredictable.

Most production systems use two or three patterns in combination, a supervisor at the top, router decisions inside the supervisor, and pipeline processing for ingestion.

FAQ

When is a multi-agent system overkill?

When one agent with three tools can do the job. The classic mistake is wrapping a single-agent workflow in three agents that pretend to collaborate, just because “multi-agent” sounds more sophisticated. Start single-agent. Add specialization only when the eval suite shows the single agent failing on a specific axis.

How is this different from an AI workforce?

An AI workforce is the business framing, a team of agents covering executive, operational, and task tiers in your organization. A multi-agent system is the technical architecture that delivers that workforce. The first is a buyer’s mental model; the second is the engineer’s blueprint.

Do all agents in a multi-agent system have to use the same model?

No. A common pattern is mixing models per specialty, Claude for reasoning-heavy work, smaller cheaper models for classification, GPT or Gemini where their strengths fit. The orchestration layer is model-agnostic; each agent makes its own model choice. See our framework comparison for which orchestration tools support this best.