Multi-Agent Systems.

The appeal is obvious: specialisation. A narrowly scoped agent is easier to evaluate, easier to guard, and more reliable than one agent trying to do everything. The cost is equally real: coordination, shared state, and cross-agent failure modes that don't exist in a single-agent design.

• Coordinator-worker — a lead agent breaks a goal into subtasks and delegates to specialist agents, then assembles their results. The most common pattern in production because the coordinator gives you one place to enforce policy.
• Pipeline — agents run in a fixed sequence, each one's output becoming the next one's input. Simple to reason about, but rigid — a step that needs to loop back doesn't fit cleanly.
• Debate or review — one agent produces a result, another critiques or verifies it before it ships. Effective for reducing error on high-stakes outputs, at the cost of roughly doubling inference per task.

For a deeper look at how the components inside each of these agents fit together, see our AI agent architecture guide.

• The workflow genuinely spans domains that need different tool access or different guardrails.
• A single agent's context window can't hold the full task without losing earlier steps.
• You need an independent check on a high-stakes output before it ships.

If none of these apply, a single well-scoped agent will almost always ship faster, cost less, and be easier to debug than a multi-agent system built because the problem sounded complex enough to deserve one.

• Shared state — agents need a consistent view of what's already happened, or they duplicate or contradict each other's work.
• Conflicting actions — two agents writing to the same record without awareness of each other is a real production failure mode, not a hypothetical one.
• Cost compounding — each additional agent in a chain adds its own inference cost and its own latency; a five-agent pipeline is significantly slower and more expensive than it looks on a whiteboard.
• Debugging depth — a failure three agents downstream from its root cause takes longer to trace than a single agent's misstep.

• Give the coordinator, not individual specialists, ownership of budget and step limits.
• Validate a specialist's output against an expected schema before passing it downstream.
• Log every inter-agent handoff with the same rigor as a tool call — it's the first place to look when something goes wrong.
• Start with two agents, not five. Add a third only when the first two are stable under real traffic.