What is a multi-agent system?
One agent with one set of instructions works well for a focused task. As tasks grow, a single agent's instructions get long, its tool list gets crowded, and its context fills with details that matter to only one step. A multi-agent system breaks that up, giving each agent a narrower job and only the tools and context that job needs.
Agentic orchestration describes the business side of this: agents across tools handing work to one another. Multi-agent systems are the engineering underneath, covering how the agents are arranged, how they pass work, and what happens when one fails.
What are the common multi-agent designs?
| Design | How it works | Good for |
|---|---|---|
| Planner and specialists | A planning agent breaks the task down and assigns steps to specialist agents | Tasks with clear, separate parts |
| Pipeline | Each agent does one stage and passes its output to the next | Repeatable processes, such as research, then draft, then check |
| Reviewer | One agent produces work, and another critiques it against the rules | Work where quality checks matter, like brand or compliance |
| Parallel | Several agents work on parts at once, and the results are merged | Research across many sources or markets |
What is an example of a multi-agent system?
Preparing a quarterly business review
A planning agent splits the request into parts. One agent pulls performance from the governed data, another summarizes creative results, and a third drafts the narrative. A reviewer agent checks every number in the draft against the data.
The strategist sees the plan before it runs and the draft before it goes anywhere. When the reviewer flags a figure that doesn't match, the drafting agent corrects it before a person reads it.
Single agent vs. multi-agent system
| Single agent | Multi-agent system | |
|---|---|---|
| Setup | One set of instructions and tools | Several agents, roles, and handoffs |
| Debugging | Easier, with one place to look | Harder, since an error can start in any agent |
| Cost and speed | Lower | Higher, with more model calls per task |
| Best when | The task is focused | Parts need different tools, context, or rules |
When does a multi-agent system make sense?
Adding agents adds handoffs, and each handoff is a chance to lose context. Each agent also adds model calls, time, and cost. Start with one agent, and split only when part of the task clearly needs its own tools, context, or rules.
- Pass context on purpose. Decide what each agent hands the next one, and fix it as a set format.
- Show the plan. A wrong plan sends every later step the wrong way, so let a person check it first.
- Fail safely. If one agent fails, the system should stop and ask a person before passing anything along.
- Log every handoff. When something goes wrong, the log shows which agent started it.
How does Alli do this?
In Alli by PMG, Ask Alli and Alli4u are the agentic layer. They help teams build automations by finding data sources, writing queries, and handling cases like a source that doesn't exist. Agents across Alli Intelligence, Alli Planning, and Alli Activate work from the same governed data, so a handoff between them doesn't change what a metric means.
Once a process is proven, Alli Generative Workflows runs it as fixed steps in the same order every time, and a person approves a change before it goes further. Outside agents can join through Alli MCP Server, using only the tools each user's permissions allow.
Explore Alli Generative WorkflowsMulti-agent systems FAQ
What is the difference between a multi-agent system and agentic orchestration?
They describe the same idea from two sides. Agentic orchestration is agents in different tools handing work to one another across a business process. A multi-agent system is the technical design: the agents, their roles, and how they pass work.
When should you use more than one agent?
When parts of a task need different tools, context, or rules, or when a separate reviewer improves quality. For a focused task, one agent is cheaper and easier to debug.
What is a planning agent?
An agent whose job is to break a goal into steps and assign each step to the right agent or tool. It usually doesn't do the work itself.
Why do multi-agent systems fail?
Most often at handoffs. An agent passes on too little context, or passes on a mistake that the next agent treats as fact. Set handoff formats, review steps, and logs reduce both.
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