Multi-agent systems: how to fix AI agent degradation

The growing-agent trap

You build an AI agent. You connect your first MCP server — say, for working with a calendar. Everything works great: the agent understands requests clearly, calls the right functions with the right parameters, answers to the point. You are thrilled.

Then you connect a second server — for email. Still not bad, but sometimes the agent confuses parameters: it tries to send an email using the calendar's date format. Fine, you tweak the prompt.

Third server — CRM. Fourth — knowledge base. Fifth — payment system. And that's where the chaos begins.

What exactly breaks

The problem isn't the number of tools per se. The problem is that every tool brings its own context, its own rules, and its own expectations — and all of it gets dumped into a single system prompt for a single agent.

Here are the concrete symptoms you start to notice:

The pattern you're not noticing

Now stop and think: at what moment does the user reach for each of your tools?

A user asking about a payment almost never asks, in the same request, to plan their day. Someone searching the knowledge base isn't simultaneously checking sales analytics.

Once you see those clusters, it becomes obvious: your agent is internally performing several business functions. And each of them could run autonomously.

The solution: divide and conquer

Each tool group becomes a separate agent with its own prompt, its own rules, and its own limited set of tools. The billing agent only knows about billing. The planner agent only knows about the calendar and email. No context conflicts, no parameter pollution.

But a logical question arises: if I split one agent into several, how will the user actually talk to them? You can't make them choose which agent to address.

And here the answer is obvious — an orchestrator agent. A separate agent whose only job is to figure out what the user wants and route the request to the right specialist.

Two types of multi-agent systems

I've come to see two fundamentally different approaches to organizing multi-agent systems. The choice between them depends on how tightly your agents need to be connected to one another.

Linear system (routing)

The simplest variant. Every user request meets a router agent, which determines the category and forwards the entire request to one specific agent. One request — one agent. No overlap.

This works well when your agents have nothing to do with each other. For example, a simple personal assistant: you have a planner agent and a workout-program agent. When you ask for a workout plan, you probably don't care what's on your work calendar. Router classified it — workouts, forwarded the request — got the answer. Done.

Recursive system (orchestration)

Things get more interesting here. The orchestrator isn't just a router — it's a full-fledged agent that can call subordinate agents as many times as it deems necessary. It can gather information from one agent, use that to call another, and only then produce the final answer.

Take the same personal-assistant example. The user asks: "Plan my day." In a linear system, the request would go to the planner — and that's it. But in a recursive system, the orchestrator can first ask the workout agent: "What's today's workout?" Get the answer: "Heavy leg day, about 90 minutes." And then, armed with that, ask the planner: "Build a daily plan, accounting for a heavy workout from 6:00 to 7:30 PM — the user will need rest afterwards."

How to choose?

In practice, many teams start with a linear system and move to a recursive one as scenarios grow more complex. That's a perfectly normal path.

Try it yourself

Multi-agent systems aren't a buzzword from research papers. They're a practical tool that solves a concrete problem: quality degradation as an agent scales up.

By splitting one overloaded agent into several specialized ones, you gain predictability, quality, and the ability to evolve each component independently.