A multi-agent system is an AI architecture in which several agents, each with its own role, tools, and instructions, work together on a task that would strain a single agent. A common pattern is an orchestrator that decomposes the goal and delegates: a research agent gathers sources, an analyst agent synthesizes them, a critic agent reviews the draft, and the orchestrator assembles the result. Other patterns include peer debate, where agents argue toward a better answer, and pipelines, where work flows through specialists in sequence.
The case for splitting work across agents is focus. A single agent juggling twenty tools and a sprawling objective dilutes its context and makes more mistakes; a specialist with three tools and one job is more reliable, easier to test, and easier to improve in isolation. Subagents also parallelize, exploring multiple leads at once, and a reviewer agent catches errors a producer agent cannot see in its own work. The costs are real: token usage multiplies, coordination failures replace single-agent failures, agents can compound each other's mistakes, and debugging means tracing a conversation among machines. Frameworks like LangGraph, CrewAI, and AutoGen, and emerging protocols for agent-to-agent communication, exist to manage exactly this complexity.
At arosplatforms we hold a firm rule: one agent until proven otherwise. When a workflow genuinely needs multiple agents, we design explicit handoff contracts between them, trace every inter-agent message for debuggability, and evaluate the system end to end rather than agent by agent, because a team of individually competent agents can still fail as a team.