When to Build a Multi-Agent System (and When to Avoid It)
Practical criteria for choosing between single and multi-agent architectures, with clear dos and don'ts.
Multi-agent architectures solve specific coordination problems—most projects don’t need them.
What actually changes when you add more agents?
Adding a second agent introduces three hard problems: shared context, handoff consistency, and error isolation. A single agent with good tooling can often handle the workload without these costs. The real question isn’t about scaling up, but about whether your task has natural boundaries that benefit from separation.
When does Claude Agent SDK make sense over LangGraph?
Claude Agent SDK works best when you’re already using Anthropic’s models and need tight integration with their tool-use pipeline. LangGraph is better for projects that mix models (like GPT-4 + Claude) or need custom coordination logic. If you’re not sure, start with the SDK—it’s simpler to refactor later than to over-engineer upfront.
Why would anyone use MCP instead of plain APIs?
MCP servers shine when you have multiple agents accessing the same tools or data sources. They centralize authentication, rate limiting, and tool versioning. For solo agents or simple workflows, plain APIs are faster to implement and debug. The tradeoff is maintenance: MCP adds overhead today to reduce headaches tomorrow.
What tasks absolutely require multiple agents?
Only two scenarios justify the complexity: real-time collaboration (like a coding pair) and physically distributed work (like IoT device fleets). Even then, start with one agent and split it only when you hit clear bottlenecks. Most ‘multi-agent’ demos are single agents with fancy frontends.
How do you test if you need multiple agents?
Build the entire workflow with one agent first. If you find yourself writing ‘if role == researcher:’ conditions or struggling with conflicting tool calls, those are split points. Parallelism alone isn’t a reason—modern LLMs handle concurrent tool use fine.
FAQ
Can’t I just add agents to make things faster? No. LLM parallelism doesn’t work like CPU cores. More agents usually mean more overhead, not more throughput.
What’s the biggest mistake teams make with multi-agent? Assuming it’s the ‘advanced’ path. Single-agent systems often outperform because they avoid coordination costs.
When is MCP overkill? When your tools don’t change often or aren’t shared across projects. YAML configs work fine for stable setups.