Custom Multi-Agent System

“Build us a multi-agent system” sometimes sounds like an end in itself — but several agents aren’t always needed. One agent copes as long as the task fits one set of tools and one prompt. A multi-agent system is justified where the roles are genuinely different and one agent isn’t enough: one searches, another checks, a third acts, and mixing them in one prompt loses quality and control.

What it is and when it’s justified

It’s several specialized agents with explicit roles, contracts between them and a coordinating layer on top. Each agent solves a narrow task with its own tools; they talk through defined data schemas, not free text. The control layer holds the process state, distributes tasks and handles failures. Such a system makes sense when there really are many roles, the process is long and the steps depend on each other — otherwise it’s overkill that adds complexity for nothing.

Why contracts and observability, not “a bigger prompt”

The temptation to solve everything with one bloated prompt is understandable and misleading: it quickly becomes unreadable, and changing one instruction breaks another. Splitting into specialized agents with contracts makes the system debuggable: each agent can be replaced, evaluated and tested individually, a schema mismatch shows up at once rather than surfacing three steps later. At every step there’s tracing and a handoff point to a human on hard decisions.

The coordinating layer here is the orchestrator agent, and the agents themselves are built on the same principles as in AI systems development: explicit contracts, observability, cost control. We start with the minimal solution and grow to several agents only when the process demands it.