Multi-Agent Systems

Instead of building one giant agent that knows how to do everything, you can build several smaller, specialized agents that collaborate — exactly like a human team instead of one jack-of-all-trades.

What Is a Multi-Agent System?

A multi-agent system consists of several independent agents, each with a different responsibility, set of tools, and sometimes even a different language model, interacting with each other to reach a shared goal. For example, in an online store: a product search agent, an inventory-checking agent, and a payment-processing agent — each specialized in one domain.

Why Isn't One Do-Everything Agent Enough?

As the number of tools and responsibilities on one agent grows, several problems appear: the system prompt becomes excessively long and confusing, the model makes mistakes choosing the right tool among dozens of options, and debugging faulty behavior becomes difficult because everything is centralized in one "brain." Splitting responsibility across several smaller agents, each with a limited toolset and a focused prompt, reduces these problems.

Benefits

• Specialization — each agent performs more accurately within its own narrow domain
• Development scalability — different teams can work independently on different agents
• Easier debugging — you can pinpoint exactly which agent was responsible for a wrong decision
• Reuse — a specialized agent (like a payment agent) can be used across several different scenarios

Challenges

• Coordination overhead — exchanging messages between agents adds latency and complexity
• Who's ultimately responsible? — without a clear orchestration layer (see Agent Orchestration), decision-making can get confused
• Compounding errors — if the first agent makes a mistake, subsequent agents build on that same mistake
• Compute cost — several model calls instead of one raises cost and latency

Shared vs. Isolated Memory/Context

One of the fundamental architectural decisions in designing a multi-agent system is: do all agents access a single shared State, or does each have its own independent, isolated Context and exchange information only through defined messages?

• Shared State (common in frameworks like LangGraph) — all nodes can read and update one shared data object. Simpler to implement, but the risk of interference (one agent mistakenly modifying data belonging to another agent) is higher.
• Isolated Context (common in communication between different organizations, like A2A) — each agent only sees the information explicitly given to it in a Message. Safer and more scalable for independent agents, but requires more careful message design since no implicit data is shared.

A general rule: within one team/organization where you trust all agents, shared State speeds up development; at the boundary between different organizations or third-party agents, isolating context is a security requirement, not just a style choice.

When Should You Consider a Multi-Agent System?

If a single agent with a few tools works well, that's enough — don't add complexity. Consider going multi-agent when: the number of tools/responsibilities has grown large and heterogeneous, the work domains are clearly separate (like search vs. payment), or you need to scale each part independently. See common implementation patterns in Multi-Agent Architecture Patterns.

FAQ