A multi-agent system uses several specialised AI agents that each handle part of a task, coordinated by an orchestrator — like a team rather than a soloist.
A multi-agent system is what you get when one big agent isn't the right answer. Instead of asking a single agent to do everything, you build a team — one agent triages, one drafts, one fact-checks, one escalates to a human when needed. An orchestrator coordinates them, deciding which agent handles which step.
The reason for splitting work this way is the same reason you split work between humans: specialisation produces better outcomes. A small agent with one job and a focused knowledge base outperforms a huge agent told to do everything. It's also easier to evaluate (you can test each piece in isolation), easier to improve (you upgrade one agent without breaking the others), and easier to debug.
The trade-off is operational complexity. Multi-agent systems have more moving parts, more places to fail, and more orchestration to think about. The right time to introduce one is when a single agent has clearly hit a ceiling — not as a starting point.
A customer-support team built as multi-agent: one agent triages incoming messages and routes by topic, one drafts replies for billing questions, one drafts replies for technical questions, one looks up account information when needed, and one escalates anything ambiguous to a human. Each agent does one thing well; the system handles a workload no single agent could touch.
As you scale agent use, the alternative to multi-agent systems is bigger and bigger single agents — and bigger single agents are increasingly slower, more expensive, harder to evaluate, and easier to break. Multi-agent systems are how production AI deployments stay sane past a certain volume.
For non-technical builders, the practical implication is that you don't have to make your first agent do everything. Start with one agent doing one thing well. When you need more, add another agent — don't try to bolt new behaviours onto the first.
The risk is over-engineering. A two-agent system is often warranted; a ten-agent system rarely is. Most multi-agent systems that fail in production fail because they were too clever for the actual problem.
Squidgy supports multi-agent systems out of the box. Our showcase Content Team — Sophia for social, Nula for the newsletter, Blake for the blog — is itself a small multi-agent system, with each agent specialising in one channel and the team coordinated under a shared brand voice and approval flow.
When you build on Squidgy, you can either ship a single focused agent or compose multiple. The orchestration layer (handoffs, approvals, shared context) is built in.
One big agent is simpler at first but breaks down at scale — slower, costlier, harder to test, harder to fix when it goes wrong. Specialised agents in a team work better at production volume.
Through an orchestrator. The orchestrator routes inputs to the right agent, hands outputs from one agent to the next, and tracks the overall goal. Modern orchestration also handles retries, escalation, and shared context.
Per task, sometimes — multiple agents means multiple model calls. But for high-volume work, multi-agent often ends up cheaper because each agent is smaller and runs on a cheaper model than a single all-purpose one would need.
When your single agent is clearly being asked to do two unrelated things, or when its eval scores start dropping as you add capabilities. If a focused agent passes its eval cleanly, don't bolt on more — split it instead.
Glossary
What is Agent orchestration?
Agent orchestration is the layer that decides which agent (or which step) handles which part of a task — coordinating multiple agents, tools, and humans toward an outcome.
Glossary
What is Agentic workflow?
An agentic workflow is a process where AI agents make some of the decisions about what to do next — instead of just executing fixed steps you laid out in advance.
Glossary
What is AI agent?
An AI agent is software that takes a goal, decides what steps to take, uses tools to do them, and carries the work out with little or no human prodding between steps.
Glossary
What is Agent eval?
Agent eval is the practice of running an AI agent against test cases to measure whether it does the job correctly — so you can ship with confidence and catch regressions.
No code. Hands-on onboarding from the team in your first cohort.