Search⌘ K
AI Features

Defending a Sub-Agent Design

Explore how to defend multi-agent AI system designs by understanding the role and boundaries of sub-agents. Learn to explain isolated context, scoped tool usage, cost trade-offs, and failure handling. Gain skills in using Agent SDK features like AgentDefinition and policy hooks to enforce boundaries and trace agent actions. This lesson helps you prepare solid defenses for sub-agent designs in technical interviews at Anthropic.

Designs with more than one agent draw some of the sharpest pushback in Anthropic’s technical rounds. An interviewer looks at the diagram and asks a short question: “Why not one agent?” A candidate who added agents because the design looked more advanced has no good answer. A candidate who added one for a specific reason can name the reason, show the boundary it creates, and state what it costs.

That answer rests on three ideas: isolated context, scoped tools, and contained failure. Each one is easier to defend when we have built it. The examples extend the expense-audit scenario, where a coordinator decides what happens to each report and a sub-agent checks each report against its scanned receipt.

When a sub-agent earns its place

A sub-agent is a specialized agent that a main agent, the coordinator, hands part of a task to. The sub-agent works in its own context window with its own instructions and tools, then reports a result back. The coordinator sees that result and nothing else from the sub-agent’s work.

Sub-agent
Sub-agent

A sub-agent earns its place in a Claude design for four reasons.

  • Isolated context: A noisy subtask, such as reading long receipts or search results, stays in the sub-agent’s context, and the coordinator receives only the finding.

  • A narrower tool set: A role that only needs to read data gets read tools only, so it cannot take actions even when it makes a mistake.

  • Parallel work: Independent checks, such as one receipt check per report, can run at the same time.

  • A cheaper model: A focused task often runs well on a smaller, faster model than the coordinator needs.

Every sub-agent also adds cost. Each one starts its own conversation, rereads its instructions, and spends tokens the coordinator would not have spent. Strong candidates state that cost before the ...