Multi-agent systems are overhyped.

Let's dive deep:

If you've been anywhere near the AI discourse this year, you've heard the term "multi-agent." It's become a catch-all for anything that involves more than one AI step. Most of the time, what people call multi-agent is really just a sequential pipeline with a language model at each stage. That's fine. It's useful. But it's not what makes multi-agent systems genuinely powerful.

The real value of multi-agent architecture shows up when you have tasks that require different capabilities, different context windows, and different decision boundaries — running in parallel, not in sequence. Think of it like a team, not a conveyor belt.

In Relay, our multi-agent system works like an actual operations team. One agent handles triage — reading incoming requests, classifying them, routing them. Another handles execution — processing refunds, updating records, sending notifications. A third handles oversight — monitoring for anomalies, checking compliance rules, flagging things that look off.

"We went from one monolithic automation that broke weekly to three agents that haven't needed a single fix in two months." — David Okafor, Principal Engineer at Conduit

These agents don't just pass data down a chain. They communicate. The triage agent can escalate directly to the oversight agent if something looks unusual, skipping the execution step entirely. The execution agent can request more context from triage if a request is ambiguous. It's a mesh, not a pipe.

That said, most use cases don't need this. If your workflow is linear — step one, step two, step three — a single agent with good tool access is simpler and more reliable. We only deploy multi-agent when the workflow genuinely has branching logic, parallel concerns, or conflicting objectives that need to be balanced.