What Is Multi-Agent System?
Also known as: multi-agent AI, agent orchestration, agent swarm
Definition
A multi-agent system uses several specialised AI agents, each with its own instructions and tools, coordinated by an orchestrator to complete a larger task. For example, separate agents for inventory, suppliers and finance feed one recommendation.
Multi-Agent System Explained
A single agent with dozens of tools and a long prompt tends to pick the wrong tool and lose track of the goal. A multi-agent design splits the job. A planner or orchestrator breaks a request into sub-tasks, hands each to a specialist agent with a narrow toolset, and combines their outputs. A procurement agent sees supplier and purchase order tools, an inventory agent sees stock and planning tools, and a finance agent sees cost and payables tools.
Common patterns include orchestrator and workers, a pipeline where one agent's output feeds the next, and a reviewer agent that checks another agent's draft against rules before a human sees it. Each agent can use a different model: a small fast model for routine lookups and a larger model for planning.
The costs are real. More agents mean more model calls, more latency, harder debugging and more places for errors to compound. Many ERP tasks, such as answering a question over live data, work well with one agent and good tools. Multi-agent designs earn their place when tasks are long, cross functional boundaries, or need independent checking.
Governance gets harder as well. Every agent needs scoped permissions, every handoff should be logged, and writes should still pass through a human approval step. Agents that talk to each other can also pass along injected instructions, so treat inter-agent messages as untrusted input.
Why It Matters
- Narrow toolsets improve tool-selection accuracy compared with one overloaded agent.
- A reviewer agent adds an automated check before humans spend time on a draft.
- Cross-functional ERP workflows, such as shortage to expedite to cost impact, map naturally onto specialists.
- Complexity and cost rise quickly, so it should be adopted only where one agent falls short.
In Practice
A weekly shortage review runs three agents. The planning agent lists components short for jobs due within 10 days. The supplier agent checks open PO dates and past delivery performance for each short item. The cost agent estimates expedite charges versus line-down cost. The orchestrator merges the results into one ranked list of actions for the buyer, who approves each purchase change.
Frequently Asked Questions
Do I need multiple agents for ERP automation?
Often not. Start with one agent and well-designed tools, and split into specialists only when tool confusion, prompt size or the need for independent review becomes a problem.
Can different agents use different models?
Yes. Routine retrieval can use a small local model while planning or review uses a larger one, which controls cost and GPU load.
How do I stop agents making things worse together?
Give each agent least-privilege tools, log every handoff, set step and cost limits, and keep human approval on writes.
What is the main risk?
Compounding errors and injected instructions passing between agents. Treat agent outputs as untrusted input and validate before acting.
Related Terms
AI Agent
An AI agent is a system that wraps a language model with tools, memory, and a goal so it can plan a sequence of steps, call external systems, observe the results, and iterate until the objective is met or a limit is reached.
MCP (Model Context Protocol)
MCP (Model Context Protocol) is an open standard that defines how AI applications connect to external tools and data sources, so any compliant client can use any compliant server without custom integration code for each pair.
Human-in-the-Loop (HITL)
Human-in-the-loop (HITL) is a design where an AI system proposes an action, such as creating a purchase order, and a person reviews and approves it before it executes. It keeps accountability with people while automating the preparation work.
AI Guardrails
AI guardrails are the technical and policy controls placed around an AI system that constrain what it may receive as input, what it may produce as output, and what actions it is permitted to take, independent of what the model itself decides.
Function Calling (Tool Calling)
Function calling, also called tool calling, is a capability where an LLM outputs a structured request to run a named function with specific arguments, such as get_order_status(order_id). Your software executes it and returns the result to the model.
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