Multi-Agent System

This division increases reliability compared to a single generalist agent: each sub-agent has a narrower, more controllable scope of work with its own system prompt and dedicated tools — and mistakes made by one agent can be caught by another, such as through a dedicated review agent.

The principle mirrors teamwork: you wouldn't expect one person to excel simultaneously at accounting, sales, and quality assurance — specialization works just as effectively for agents.

In more complex projects — when multiple systems, languages, or approval stages interact — an orchestrator agent often takes charge of coordination: it breaks down the task, assigns specialists, collects results, and decides the next step. Standards like the Model Context Protocol simplify the uniform integration of tools.

Critical for operations: even in multi-agent systems, every action remains logged and critical steps go through human approval — more agents mean more structure, not less control.

In practice, certain architectural patterns recur: the pipeline (agents work sequentially — capture, enrich, draft, review), the orchestrator with specialists (a coordinator distributes to expert agents and consolidates results), and the critic pattern (one agent generates, a second reviews specifically for errors and violations). The critic pattern in particular often significantly lifts quality — similar to the four-eyes principle in the office.

For getting started, though, complexity must be earned. Most successful projects begin with a single agent and grow into a multi-agent system only when the process genuinely demands it — not because the architecture sounds more impressive.

Multiple agents bring not just advantages — they create their own risks you need to understand. The most critical is error propagation: if an early agent in the chain makes an undetected mistake, the downstream agents work from a false foundation and may amplify it. A research agent that brings in false information can produce a factually sound but substantively incorrect final output. That's precisely what the critic or review pattern guards against — it's not a luxury in multi-step chains, it's a necessity.

Add to this traceability and operations: with a single agent, it's easy to see what went wrong; with multiple agents interacting, you need to reconstruct which agent decided what on what basis. That's why complete logs at each step are mandatory, not optional. Response time and cost also tend to rise because multiple model calls run in sequence. The planning consequence: a multi-agent system must be thoughtfully designed — as few handoffs as possible, clear checkpoints at critical junctures, and comprehensive logging throughout. With that discipline in place, you capture the benefits of division of labor without shouldering the drawbacks.